diff --git a/task0/LinearRegression_soner.ipynb b/task0/LinearRegression_soner.ipynb new file mode 100644 index 0000000..c2646cc --- /dev/null +++ b/task0/LinearRegression_soner.ipynb @@ -0,0 +1,1911 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "r79-WpxPbR9j" + }, + "source": [ + "# Intro to Linear Regression\n", + "## BogaziciAI\n", + "\n", + "Linear regression is the process of modelling a linear relationship between variables." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Oxy799JKbR9k" + }, + "source": [ + "In order to understand it clearly, we can define a custom dataset, that includes a linear relationship between 2 variables.\n", + "Let's assume that our dataset is linearly correlated as Y=AX+B ideally." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "executionInfo": { + "elapsed": 1369, + "status": "ok", + "timestamp": 1624863835659, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "jD8FXGi4bR9l" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import linear_model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oLVp1RVAbR9l" + }, + "source": [ + "First create a dataset that has disturbances." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 269 + }, + "executionInfo": { + "elapsed": 8, + "status": "ok", + "timestamp": 1624863835663, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "0erJeSMSbR9m", + "outputId": "8f0b30aa-e4cc-4037-eb99-ce0b56407b28" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "A = 2\n", + "B = -20\n", + "x_range = [-20, 100]\n", + "\n", + "def load_dataset(n=150):\n", + " x = (x_range[1] - x_range[0]) * np.random.rand(n) + x_range[0]\n", + " eps = np.random.normal(0,10,n)\n", + " \n", + " y = A * x + B + eps\n", + " return np.array(y).reshape(-1,1), np.array(x).reshape(-1,1)\n", + "\n", + "\n", + "y, x = load_dataset()\n", + "\n", + "plt.plot(x,y,'*')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xeqEGSM8bR9n" + }, + "source": [ + "As data seem to be correlated linearly, (We already know that in this situation as it is created by us, but we need to inspect unkown datasets in order to choose an eligable method.) it is appropriate to apply linear regression." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 286 + }, + "executionInfo": { + "elapsed": 370, + "status": "ok", + "timestamp": 1624863873272, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "fGnrz0PmbR9n", + "outputId": "9c67c289-78dc-40dc-829c-88224ec532b7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A: 2.01\t\tB: -20.04\tR2: 0.981\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "regression = linear_model.LinearRegression()\n", + "regression = regression.fit(x,y)\n", + "print(f\"A: {regression.coef_[0][0]:.2f}\\t\\tB: {regression.intercept_[0]:.2f}\\tR2: {regression.score(x,y):.3f}\")\n", + "\n", + "x_pred = np.array(x_range).reshape(-1,1)\n", + "y_pred = regression.predict(x_pred)\n", + "plt.plot(x_pred,y_pred,'r-',x,y,'*')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zkuwExtWbR9n" + }, + "source": [ + "# Load CVS file with PANDAS " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "en66MvAxbR9n" + }, + "source": [ + "In this section, you are given weather data. You will use pandas dataframe to load csv file. Observe dataframe shape and by using .head(), investigate the content of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 408 + }, + "executionInfo": { + "elapsed": 25, + "status": "error", + "timestamp": 1624863836155, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "M_RQaBDtbR9o", + "outputId": "6c5c4758-ae13-4d63-da0f-58d7c40b2508", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1999, 12)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "WeatherData=pd.read_csv('weatherHistory.csv') \n", + "WeatherData.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "executionInfo": { + "elapsed": 11, + "status": "aborted", + "timestamp": 1624863836142, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "XqYQMS4NbR9o" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Formatted DateSummaryPrecip TypeTemperatureApparent TemperatureHumidityWind SpeedWind BearingVisibilityLoud CoverPressureDaily Summary
02006-04-01 00:00:00.000 +0200Partly Cloudyrain9.4722227.3888890.8914.119725115.826301015.13Partly cloudy throughout the day.
12006-04-01 01:00:00.000 +0200Partly Cloudyrain9.3555567.2277780.8614.264625915.826301015.63Partly cloudy throughout the day.
22006-04-01 02:00:00.000 +0200Mostly Cloudyrain9.3777789.3777780.893.928420414.956901015.94Partly cloudy throughout the day.
32006-04-01 03:00:00.000 +0200Partly Cloudyrain8.2888895.9444440.8314.103626915.826301016.41Partly cloudy throughout the day.
42006-04-01 04:00:00.000 +0200Mostly Cloudyrain8.7555566.9777780.8311.044625915.826301016.51Partly cloudy throughout the day.
\n", + "
" + ], + "text/plain": [ + " Formatted Date Summary Precip Type Temperature \\\n", + "0 2006-04-01 00:00:00.000 +0200 Partly Cloudy rain 9.472222 \n", + "1 2006-04-01 01:00:00.000 +0200 Partly Cloudy rain 9.355556 \n", + "2 2006-04-01 02:00:00.000 +0200 Mostly Cloudy rain 9.377778 \n", + "3 2006-04-01 03:00:00.000 +0200 Partly Cloudy rain 8.288889 \n", + "4 2006-04-01 04:00:00.000 +0200 Mostly Cloudy rain 8.755556 \n", + "\n", + " Apparent Temperature Humidity Wind Speed Wind Bearing Visibility \\\n", + "0 7.388889 0.89 14.1197 251 15.8263 \n", + "1 7.227778 0.86 14.2646 259 15.8263 \n", + "2 9.377778 0.89 3.9284 204 14.9569 \n", + "3 5.944444 0.83 14.1036 269 15.8263 \n", + "4 6.977778 0.83 11.0446 259 15.8263 \n", + "\n", + " Loud Cover Pressure Daily Summary \n", + "0 0 1015.13 Partly cloudy throughout the day. \n", + "1 0 1015.63 Partly cloudy throughout the day. \n", + "2 0 1015.94 Partly cloudy throughout the day. \n", + "3 0 1016.41 Partly cloudy throughout the day. \n", + "4 0 1016.51 Partly cloudy throughout the day. " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "WeatherData.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "executionInfo": { + "elapsed": 12, + "status": "aborted", + "timestamp": 1624863836143, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "TFUHEKLYbR9o" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1999 entries, 0 to 1998\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Formatted Date 1999 non-null object \n", + " 1 Summary 1999 non-null object \n", + " 2 Precip Type 1999 non-null object \n", + " 3 Temperature 1999 non-null float64\n", + " 4 Apparent Temperature 1999 non-null float64\n", + " 5 Humidity 1999 non-null float64\n", + " 6 Wind Speed 1999 non-null float64\n", + " 7 Wind Bearing 1999 non-null int64 \n", + " 8 Visibility 1999 non-null float64\n", + " 9 Loud Cover 1999 non-null int64 \n", + " 10 Pressure 1999 non-null float64\n", + " 11 Daily Summary 1999 non-null object \n", + "dtypes: float64(6), int64(2), object(4)\n", + "memory usage: 187.5+ KB\n" + ] + } + ], + "source": [ + "WeatherData.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "executionInfo": { + "elapsed": 13, + "status": "aborted", + "timestamp": 1624863836144, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "VGVgF0SRbR9o" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Temp=WeatherData.Temperature\n", + "Hum=WeatherData.Humidity\n", + "type(Temp)\n", + "type(Hum)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nDllmPf6bR9o" + }, + "source": [ + "Data visualization is cruicial to understand the quality of the data. You need to observe inputs and outputs to form a hypothesis about possible correlations. Investigate matplotlib. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "executionInfo": { + "elapsed": 13, + "status": "aborted", + "timestamp": 1624863836144, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "bpLjZrgRbR9o" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "fig = plt.figure()\n", + "ax1 = fig.add_subplot(2, 1, 1) # two rows, one column, first plot\n", + "ax1.set_ylabel('Temperature (C)')\n", + "ax1.set_xlabel('1 hr intervals (h)')\n", + "ax1.set_title(' Hourly Temperature-Humidity')\n", + "ax1.plot(Temp[0:300])\n", + "\n", + "ax2 = fig.add_subplot(2, 1, 2) # two rows, one column, first plot\n", + "ax2.set_ylabel('Humidity ')\n", + "ax2.set_xlabel('1 hr intervals (h)')\n", + "ax2.plot(Hum[0:300])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cEsq7PU9bR9o" + }, + "source": [ + "Lets scatterplot the same data to see different kinds of visualizations. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "executionInfo": { + "elapsed": 13, + "status": "aborted", + "timestamp": 1624863836145, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "V8UHn4JrbR9p" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Humidity (%)')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "plt.scatter(Temp[0:300],Hum[0:300])\n", + "plt.xlabel('Temperature (C)')\n", + "plt.ylabel('Humidity (%)')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JzdRYicRbR9p" + }, + "source": [ + " Optional Exercise : Use .scatter(Temp[0:300], WindSpeed[0:300], Hum[0:300]) to make a 3d plot including windspeed for the first 300 data points. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "executionInfo": { + "elapsed": 14, + "status": "aborted", + "timestamp": 1624863836146, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "ei-LSfYNbR9p" + }, + "outputs": [], + "source": [ + "#ax = plt.figure().add_subplot(projection='3d')\n", + "\n", + "#ax.\n", + "\n", + "#ax.set_zlim(0, 1)\n", + "\n", + "#ax.set_xlabel('Temperature (C)')\n", + "#ax.set_ylabel('Wind Speed')\n", + "#ax.set_zlabel('Humidity')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ug_FulbebR9p" + }, + "source": [ + "# Real Estate Data, Linear and Nonlinear Regression " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "executionInfo": { + "elapsed": 14, + "status": "aborted", + "timestamp": 1624863836146, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "S87_N0WMbR9p" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(428, 8)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "################# Dosya ismi Realestate.csv, panda dataframe olarak yukleyin\n", + "HouseData= pd.read_csv('Realestate.csv') \n", + "#################\n", + "HouseData.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "executionInfo": { + "elapsed": 15, + "status": "aborted", + "timestamp": 1624863836147, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "DM7yGUC-bR9p" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 428 entries, 0 to 427\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 No 428 non-null int64 \n", + " 1 transactiondate 428 non-null float64\n", + " 2 houseage 428 non-null float64\n", + " 3 distancetostation 428 non-null float64\n", + " 4 numberofstores 428 non-null int64 \n", + " 5 latitude 428 non-null float64\n", + " 6 longitude 428 non-null float64\n", + " 7 housepriceofunitarea 428 non-null float64\n", + "dtypes: float64(6), int64(2)\n", + "memory usage: 26.9 KB\n" + ] + } + ], + "source": [ + "HouseData.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "executionInfo": { + "elapsed": 17, + "status": "aborted", + "timestamp": 1624863836149, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "l3JlxtAUbR9p" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " No transactiondate houseage distancetostation numberofstores latitude \\\n", + "0 0 2012.1 15.0 88.0 3 1.0 \n", + "1 0 2012.1 12.0 300.0 3 1.0 \n", + "2 0 2012.1 14.0 658.0 5 1.0 \n", + "3 0 2012.1 20.0 204.0 7 1.0 \n", + "4 0 2012.1 22.0 1020.0 6 1.0 \n", + "\n", + " longitude housepriceofunitarea \n", + "0 2.0 195.4 \n", + "1 2.0 85.7 \n", + "2 2.0 44.9 \n", + "3 2.0 135.6 \n", + "4 2.0 33.5 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "HouseData.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iB0IKqLnbR9q" + }, + "source": [ + "# Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8QZdj1iqbR9q" + }, + "source": [ + "Example: run the code in 3 sections in order for ToNthHouse=10 and ToNthHouse=14 and answer 3 questions below " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "executionInfo": { + "elapsed": 16, + "status": "aborted", + "timestamp": 1624863836149, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "gZcfnx57bR9q" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 220.0)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "Dist=HouseData['distancetostation'].to_numpy()\n", + "Price=HouseData['housepriceofunitarea'].to_numpy()\n", + "Age=HouseData['houseage'].to_numpy()\n", + "\n", + "###### First Examine House Distance vs Price for the first 10 house values(ToNthHouse) then 14\n", + "\n", + "ToNthHouse=14\n", + "\n", + "######\n", + "fig = plt.figure()\n", + "plt.scatter(Dist[:ToNthHouse],Price[:ToNthHouse])\n", + "plt.xlabel('Distance to the closest Transportation(m)')\n", + "plt.ylabel('Unit Price')\n", + "plt.xlim(0,1800)\n", + "plt.ylim(0,220)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "executionInfo": { + "elapsed": 17, + "status": "aborted", + "timestamp": 1624863836150, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "uJqd1H0SbR9q" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.11621887832556999" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "reg = LinearRegression()\n", + "reg.fit(Dist[:ToNthHouse].reshape(-1, 1), Price[:ToNthHouse].reshape(-1, 1))\n", + "reg.score(Dist[:ToNthHouse].reshape(-1, 1), Price[:ToNthHouse].reshape(-1, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "executionInfo": { + "elapsed": 17, + "status": "aborted", + "timestamp": 1624863836150, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "SqzIKn4-bR9q" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "PredictedPrice=reg.predict(np.arange(0,1800,100).reshape(-1,1))\n", + "\n", + "fig = plt.figure()\n", + "plt.scatter(Dist[:ToNthHouse],Price[:ToNthHouse])\n", + "plt.xlabel('Distance to the closest Transportation(m)')\n", + "plt.ylabel('Unit Price')\n", + "plt.xlim(0,1800)\n", + "plt.ylim(0,220)\n", + "plt.plot(np.arange(0,1800,100),PredictedPrice,'r')\n", + "plt.legend(['Regression Predicted','Real Prices'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DJ-8Fr04bR9r" + }, + "source": [ + "QUESTIONS:\n", + "\n", + "1) What is the regression score (reg.score, out[177]) for ToNthHouse= 10 and 14. Write your answers below.\n", + "\n", + "\n", + "2) Outliers in regression are observations that fall far from the “cloud” of points. These points might be especially important. Which ones would be the outlier in ToNthHouse=10 and ToNthHouse=14 cases? \n", + "\n", + "\n", + "\n", + "3) Why do you think that regression score differs so much when 4 additional data points are added?\n", + "Hint: print Panda dataframe information for indices 0-14 (HouseData[0:14]) . \n", + "Do you think that linear regression Y=aX+B is useful in this case?\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1) ToNthHouse=10 ->> reg.score :0.752143701166438\n", + " ToNthHouse=14 ->> reg.score :0.11621887832556999\n", + "\n", + "2)ToNthHouse=14 is outlier, points are far from the linear line." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Notransactiondatehouseagedistancetostationnumberofstoreslatitudelongitudehousepriceofunitarea
002012.115.088.031.02.0195.4
102012.112.0300.031.02.085.7
202012.114.0658.051.02.044.9
302012.120.0204.071.02.0135.6
402012.122.01020.061.02.033.5
502012.111.0600.051.02.055.5
602012.114.0120.071.02.0185.6
702012.113.0400.031.02.065.4
802012.118.0220.051.02.0125.5
902012.117.0528.061.02.054.3
1002012.13.01250.061.02.058.0
1102012.12.01450.021.02.0100.0
1202012.11.01558.021.02.0110.0
1302012.01.01600.021.02.0120.0
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" + ], + "text/plain": [ + " No transactiondate houseage distancetostation numberofstores \\\n", + "0 0 2012.1 15.0 88.0 3 \n", + "1 0 2012.1 12.0 300.0 3 \n", + "2 0 2012.1 14.0 658.0 5 \n", + "3 0 2012.1 20.0 204.0 7 \n", + "4 0 2012.1 22.0 1020.0 6 \n", + "5 0 2012.1 11.0 600.0 5 \n", + "6 0 2012.1 14.0 120.0 7 \n", + "7 0 2012.1 13.0 400.0 3 \n", + "8 0 2012.1 18.0 220.0 5 \n", + "9 0 2012.1 17.0 528.0 6 \n", + "10 0 2012.1 3.0 1250.0 6 \n", + "11 0 2012.1 2.0 1450.0 2 \n", + "12 0 2012.1 1.0 1558.0 2 \n", + "13 0 2012.0 1.0 1600.0 2 \n", + "\n", + " latitude longitude housepriceofunitarea \n", + "0 1.0 2.0 195.4 \n", + "1 1.0 2.0 85.7 \n", + "2 1.0 2.0 44.9 \n", + "3 1.0 2.0 135.6 \n", + "4 1.0 2.0 33.5 \n", + "5 1.0 2.0 55.5 \n", + "6 1.0 2.0 185.6 \n", + "7 1.0 2.0 65.4 \n", + "8 1.0 2.0 125.5 \n", + "9 1.0 2.0 54.3 \n", + "10 1.0 2.0 58.0 \n", + "11 1.0 2.0 100.0 \n", + "12 1.0 2.0 110.0 \n", + "13 1.0 2.0 120.0 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#3)\n", + "HouseData[0:14]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#3) Because the additional 4 points are aligned on reverse direction from the our initial ten points.\n", + "plt.scatter(Dist[:10],Price[:10])\n", + "plt.scatter(Dist[10:ToNthHouse],Price[10:ToNthHouse],color ='red')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "executionInfo": { + "elapsed": 17, + "status": "aborted", + "timestamp": 1624863836150, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "yK5c_VB3bR9r" + }, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CYehxx82bR9r" + }, + "source": [ + "# Nonlinear Nth Degree poly. Regression, Y=aX^(n)+bX^(n-1)...+gX^1+h" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wlKELnygbR9r" + }, + "source": [ + "#### Example: Command N_poly_degree, change the polynomial nonlinear regression degree and observe how the scores of the predictions change \n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "executionInfo": { + "elapsed": 18, + "status": "aborted", + "timestamp": 1624863836151, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "Hg6xvt02bR9s", + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9685963206129622" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.preprocessing import PolynomialFeatures\n", + "ToNthHouse=14\n", + "N_poly_degree=1\n", + "####\n", + "N_poly_degree=3\n", + "####\n", + "poly = PolynomialFeatures(N_poly_degree)\n", + "Dist_transformed=poly.fit_transform(Dist[:ToNthHouse].reshape(-1,1))\n", + "\n", + "model = LinearRegression()\n", + "\n", + "model.fit(Dist_transformed,Price[:ToNthHouse].reshape(-1, 1) )\n", + "model.score(Dist_transformed,Price[:ToNthHouse].reshape(-1, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "executionInfo": { + "elapsed": 18, + "status": "aborted", + "timestamp": 1624863836151, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "ZHX1kkclbR9s", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "Dist_Prediction_transformed=poly.fit_transform(np.arange(0,1800,100).reshape(-1,1))\n", + "PredictedPrice=model.predict(Dist_Prediction_transformed)\n", + "\n", + "fig = plt.figure()\n", + "plt.scatter(Dist[:ToNthHouse],Price[:ToNthHouse])\n", + "plt.xlabel('Distance to the closest Transportation(m)')\n", + "plt.ylabel('Unit Price')\n", + "\n", + "plt.plot(np.arange(0,1800,100),PredictedPrice,'r')\n", + "plt.legend(['Regression Predicted','Real Prices'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Igip6MxvbR9s" + }, + "source": [ + "Question: When we switched to nonlinear regression, the prediction became better for the 14 data points, Do you think that we adopted a correct approach, is the only variable that determines the House Unit Price their distance to the closest transportation or are there any other variables that could have an influence on the House Unit Price? Write your answer here.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Answer:\n", + "I guess there will be other factors those affect the unit price. Otherwise prices should continue to fall as long as distances increase." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qc9rP-tVbR9s" + }, + "source": [ + "# THE FINAL EXERCISE: \n", + "When you look at the dataframe called HouseData, data that have \"No=0\" are fabricated by us to get you familiar with the libraries and their functions to produce simple linear and nonlinear regression models. Now, You can continue with the real data from the city Pompeii. Drop the first 14 fabricated rows from the data frame. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "executionInfo": { + "elapsed": 19, + "status": "aborted", + "timestamp": 1624863836152, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "S5ywitN1bR9s" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Notransactiondatehouseagedistancetostationnumberofstoreslatitudelongitudehousepriceofunitarea
4234102013.00013.74082.01500024.94155121.5038115.4
4244112012.6675.690.45606924.97433121.5431050.0
4254122013.25018.8390.96960724.97923121.5398640.6
4264132013.0008.1104.81010524.96674121.5406752.5
4274142013.5006.590.45606924.97433121.5431063.9
\n", + "
" + ], + "text/plain": [ + " No transactiondate houseage distancetostation numberofstores \\\n", + "423 410 2013.000 13.7 4082.01500 0 \n", + "424 411 2012.667 5.6 90.45606 9 \n", + "425 412 2013.250 18.8 390.96960 7 \n", + "426 413 2013.000 8.1 104.81010 5 \n", + "427 414 2013.500 6.5 90.45606 9 \n", + "\n", + " latitude longitude housepriceofunitarea \n", + "423 24.94155 121.50381 15.4 \n", + "424 24.97433 121.54310 50.0 \n", + "425 24.97923 121.53986 40.6 \n", + "426 24.96674 121.54067 52.5 \n", + "427 24.97433 121.54310 63.9 " + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "HouseData= pd.read_csv('Realestate.csv')\n", + "HouseData= HouseData.drop(np.arange(0,14))\n", + "\n", + "Dist=HouseData['distancetostation'].to_numpy()\n", + "Price=HouseData['housepriceofunitarea'].to_numpy()\n", + "Age=HouseData['houseage'].to_numpy()\n", + "HouseData.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Jwpshj2lbR9t" + }, + "source": [ + "\n", + "This is the final task. You are expected to create a similar linear or nonlinear regression analysis on the real data. You can choose how complicated you would like to have your investigation. Any analysis will be enough for you to fullfil this task, you do not have to present a very good correlation. After you are done, Please email us this .ipynb file updated by you, then we will welcome you to Summer Machine Learning + Deep Learning study group powered by Bogazici Uni. student and Alumni volunteers. \n", + "\n", + "\n", + "Some ideas you can work on:\n", + "\n", + "1) The simplest analysis would be to show the linear or nonlinear relation between the HouseAge and Unit Price.\n", + "\n", + "2) If you like a more challenging analysis to exercise your coding skills, You can use two or more variables such as HouseAge and Distancetostation to run a linear and nonlinear regression to predict house price of unit area.\n", + "\n", + "3) If you like a deeper analysis, you can investigate the correlation between House Unit Price distance to station when HouseAge<10 and numberofstores<3. You can also learn about correlation matrix, Download seaborn library and run the below code. According the correlation information you are getting from the heatmap, create your regression analysis. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Unit Price')" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#linear or nonlinear relation between HouseAge and Unit Price\n", + "Price=HouseData['housepriceofunitarea'].to_numpy()\n", + "Price = Price.reshape(-1,1)\n", + "Age=HouseData['houseage'].to_numpy()\n", + "Age = Age.reshape(-1,1)\n", + "fig = plt.figure()\n", + "plt.scatter(Age,Price)\n", + "plt.xlabel('House Age(year)')\n", + "plt.ylabel('Unit Price')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.04433848097791171" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "reg = LinearRegression()\n", + "reg.fit(Age, Price)\n", + "reg.score(Age, Price)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.04433848097791171" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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uX7HmiciZbndPL0dPDI76XluulUsXzYxUU7W35bjtysWj2tjn8oIKbc/iEZFV3VRKuuC4vE/FHM8oDxYfMrZJE9H8beBrwK8B7ydvC3h/JRtVSVod0+dWkUwzoGJjBPr6B5wzZ18ohV1a29ty3Hn1Ep58aV+kIJo8ITr+oRIRlFkDcOIGkKRVWZZ29nrHMyqP67ooxK6ujcYgjVA4pKr/oKp7VXW//1fxllWI65bPdW7PMgNyCZckWkWcgse1+jh8bNDZjqjt/mDb29dPuJWl+vFnVTe5BpCpbblEtVKUAIrD1EjVIe66mDqv8UkT0fykiKwFHgSO+xtV9bmKtaqC3NG1hKdf288re48Utp1z6mTu6MrPxNOqRtLEEIRpy7U6VU5xK48hVW5+cJPTcBxs3y3dm7j3qR0FFYtCoUhPR5mMslnUTS5DtgiJaqVgBGialZmpkapD0nWx69DYpBEKy73/QRdSBdyO9nXMLd2bRggEgFf2HuGW7k1cumjmCM8kn6iMpB0p0mOH6R8YcnoOpfnuxFzLKMESnPl39/SOEAg+vkCIi40IUy6XQ1do/02OLLLhVY8vgFzxFEnfNypD0nWx69C4JAoFVb20Gg2pFnHeR1FePRCd79w1A75w3lR+se2AcwArRiD49B0d4K5rlzoHa1eVNshmAym3y2HUysI1y4yzg6Q5h/ZJucR9jPJh+afGHnHBa18IbVLgbeBnqrq9oq2qIHHeR66cRb19/aNyDMUltwrOslscK4NWEYZVU81+fea0t8WqbuJmZ1lsINUoMB4lVAGOHB+MzA/lry6S+utQxvxSRmnUY/4pC6wrjbiVwnsits0H/kREblPV71WmSfVJ0BgKJ2e/UTdbcPuZq38cebxhVbavucKZiiJM+EGLuvHjZtNZVijVcDn0++f2h7eMEMa+d1ZwH/91XOEin2HF9NlVpN4yf1pgXenEleO8PWq7iEwH/gloKqHgk3XGnLS8ds2YwW0gdt3411zUEWlTAHf5zizCpdwqga5lHax9ZOuoFZqrj9PacUyfXV3qKfNnNVa5Y53Mlde8JHXF+WPWAa7BMYt6Jcugk+TX37Wsg2suir5Zgwbi4A3tuvGffGkfn7l4Xmo3VFe08aWLZlatGEiWVcmqlQvJtSRfJ9NnV5e0WQCqgQXWlU7mGs0ichlwsAJtqQouHeg1F3VEeh5FkWXQSbO8jivcHXUzx934d3QtofOM6aN+D0ZHUbuEy3ef2sG0STkmjGvhUP9ASSqB4EpkalsOkbzBvJhVif/7tz20JdI116cY4WV66OIoRl0T7Ov2STlUSX2fJV2nNPm+jHjiDM2bGJ1RYDqwC/g3lWxUJYkbpNMIBSF60Im7WZOW13GzmKjBMWkgDf+e68GNS9Nx8OgAbblW7rp2adGDY/h3gw9rb18/q77/PNd+YC4PbOhNbahM46Katb2mhy6erOqacF8HVYdJ/Z7mOqXJ92XEE7dS+FjovQL7VfVI1M6NRCk60F89a/qo75Y6qLgGeZcAcq12Ll00k6W3P1oYfKdNynHrxxc7H9ykmImohzvLjDopP9TAsPKj53dz59VLMs/SXX3W3pZz5pVyYXro4smqrkm6J/oHhrjtoS2R/Z7mOqXN92W4iTM0l7WwTiPQ7lh6Bnl9/+ibvdRBJWqQF+AzF89zejf5v+sPfpcumsm6Z3aOqA198OgAq37wPANDbjfcuChrGPlwRwm/m9Zt5MZ1GyOjpdPocfv6BzILaVfSwFyLcOTEYOEaRgnnKKHmMl73hs7dj63whWm5IsQbmaxOCWnviaW3PzpKpVSJ+iDGaDLbFMYCrtnubVcuZtX3nx8xsIbJquNPQzFufeGBdMWaJyLbPTCkzhVBR8C24BoYgw9TlPDzjxo1AKcNOEsirIN+99jgqHNt9+wVcZ5MrhWd7+UVxnc+CH/P70tTM2WPU0h7T0QJ9jQDfj3GTTQaTScUunt6+cL9G/HHlN6+fr5w/0Yg/2Cvf+NAoSpbFC0izF/94xGzxTQ5iZLULqW69cUJoKgVgf+g+L8bVT1OGJniI0nIhVdHrrQhQaYlRCDH6aCDHD426LxmfrtdKzoX/vHiVB7NrmbKOqGJc8F24fdxmjQ09RY30Yg0nVD40oMvEJ5QD2t+O8ADG3pj9ez+Z8HZYq5VyLXIiNlrOCeRS+1SLlVE3AwsuCKIE0rr3zgwKpneAxt66Twjb0dJM8sLCo44ryrIFxy69eOLY/dJW7diSNU54/eFc1a3xI6U32t2d8csE5rwoC3CqOcxil19/c77Kby9nuImGpGmEwpHB4ad2+MGoDij7MCQMm1SjknjxzlzErnULuVSRaxauTBS9eVXekvzoDz50r7YAjZpZnnB1VHcYNki+X7za1UUk7ojTDAjrE9QOLuE2rRJOY4NDDtVDknC0D/n8Grw0kUzrWxoBMF7MW198zntbc57wa+lUeu+HStuzU0nFOJw3XRCPi1FHH1HB+j58kcyHTdMKaqIKB9+3/sozfG6e3qdA5/f/nDK5LgBGOIH06D6Ls4Y7Mod5UIZ6TAwMXcyPtOlb/ZXK64HOk4Y+ucctRoMqjrM/hCN3xd/eP/zzuvs93Gc7avWfTuW3JpNKARIMmQlzRZdM4UsBtdSVBFpVgNRbYSTD1UUwdl/eJYXNzNKqz+OMwZHDRS5VmFwKDqZYHtbjuODJ1eDB48OjDi3CeNaCscOC01X34WFYZTKb8WaJzKdZz1R6xmu/1uue2XCuLxgj7ufat23Y8mt2YRCgCTPhbjZYrh+cnCmkMW4Vq7Uz3GDf7iNE3MtzralCSSL+t3g4NmSQm/sC02XCs/PKus6F7+truI9tz20heODI1VExxyqxLTn6p9v2qSGUH/2h6gZ7o3rNnL7w1tSrzLLQdwq1E+SeOfVS7jz6iXOxIi17NtqpteotBA3oRAgjeeCa7YYN1Pwi9u41C5BSii3UMC1lA3OkoNtjBNWcaU2w4Srvvmz/DSGRPHa7XqIhlULtSRuWreROe1tXHNRR0Fn76dLcHknRXmHlRqcl1YfHqTe/OVdQji4wqqGYBipMhx9zwSfJZcaqUVkVIr7alGt+IhqqKmaTii0t+U4fOQYf/roX7PijefZMfU0drbP4u0Zc2Ddu3SdeSZd/24JzJgxKjY+Tj3jqiTW29fPijVPsGrlwoJw6O7pdc52DiUEz6XBJaCyDF6Q975Je6O5qr6lRcm32/Vw+TWdgw/Dumd2csrEcSh5m04xv50UnBf3wKX1jPKpR3/5uJlstdQf4X53TYx8g7LLNbWW8SPVio+ohpqq6YTCbVcu5iv3/DP/+vlHADij7y3wY7cf/5v0Bzr1VFiwAM48E848kxteOc4L46ezc+osdk+ZyVDLySyjUXUYslYei5vBhj/LGjAmAhPHuct8piGu6ltadvX1c9e1S1OrhQaGTxZGivvttlwrE3MtkauIpOC8uAcujWogrPaqtX45HAiYRDVUMlmE66ofPM9QiqVn/8AQtz8cnS4jC2lXjnFahnKqe6qhpqqZUBCRVmA90KuqH/PqNKwjX8jndeBTqlr2bKz5i/HrXDLjH5AdO1g62Mf1s5Wlgwdg+/b832uvwYED8Qfauzf/99RTANyc5sfv8P5PmsT/mTOXDdLOG1NOZUf7aeycehp7Z8zm33/88lFfi5vBwmg7QZx6KgpViso/FLzZy6D1KlSWg/Q1nZNob8tx25V576KwsMm1CEdPDBZUDkneV1HtTRLAfjGleiBtIGAQEZjvFYry+7Icgq27pzcx220UrpQtURw8WloVvqwrR5eNrZzqnmqoqUTLocQu5ofz5T47gSmeUPhz4ICqrhGR1cA0Vf1i3DE6Ozt1/fr1FW9r+MKKDjP3+GHuWDqZD+XezQuR7dvZt+klhl7dxml9eyveJoC+SVPY2nEuz02fz+ZZZ7HptLPZ0X4aiES6i04Y1xL5EPo1G7JQjD49jrZca6z9Iosx13XccBrvIycGUw0yrv7p7ulNLBFaTN9WimL7MEiuRVj7yQucRvc0E4vunt7EdDLlIkv/h9t/5Phgqucl7rxdfV7sfRH13CU9O1GIyAZV7Yz6rCYrBRE5HbgC+Arg14K+CrjEe30P8BMgVihUi/DyVqWFHROncvNbbfx89VWF7X6wfeKN8M478PrrJ1clwRXK9u3Qn+7BbT/6DstfWc9ysgvGYYTNp53F1tPO5pzf/g145hRYsgTakmcc3T29sX7lWWkVSbypi0mPACfVPzBy9XE0pUBI8r4KR4Gn/W4tSKtiEHDGhwwMa6Q6LcuMeO0jW6siECD9OUe1P80xk8673OqeaqTxqJX66G7gjxlZB3qWqu4GUNXdInJqLRoWRdYLm2h0mjIFzj8//5eCoJAZPzjAuW+/wZK3XuUDfW9wbu8rLOx9mXGa3r0SoAXl/Lde5fy3XoWN/wh/lv67XcCFU2exxVudbJl1FptnncX+ye2Z2uDz1U9FzzxH/GboYcgypITrR6SdLacRVsGiRvWeQTWNusufuLhqi0P0fZ/FHlNN19G0apUsdo3gMW9/eEvseVdC3VPpNB5VFwoi8jFgr6puEJFLivj+DcANAPPmzSuqDVkNP1kvbLmleVDInBiXY/NpZ7Nt7kKWX72EV4BPRMygw4FZ3T293PzAC7z37V0seetVFu99jfP3vsbyA9uZcHB/5jbNO7SHeYf28Fsv/yLzd9+ZMJktsxawadbZbJ97Ll0TF8DQadDa6vxO2qW9i2LUXMOqqa5ZPeXaibu3k1ZbaVN7RN33aSZOftuqpbAOr9Ru6d5USHbZKsJ1y+dyR9eSUe1Me8zunl6nXcY/XiNmba26TUFE7gQ+CwwCE4EpwIPA+4FLvFXCbOAnqhrbc8XYFIrRyZVLj1cKSd5HUUa7YBvLots8eJDr/sN/Z/GeVzlvzzbOe2sbZx94s+Rzy8qLM+ezZfY5bJl1Fi+cuoBfnrqA/vETy/479eY5lIQr0+1nLp5XGPyCA6MItI1roX9gOPKectXiiDI4J91f5bZBuXBds1u6N0W6sa44azqv7+9PtXoM92WcjSb4XNU6YjyKOJtCzQzNAN5K4Y88Q/Na8pXdfEPzdFX947jvFyMUih0c6z3ZWdJ5ucpXCmTyjnH9TqsIX/3UBQCRMRi5oQHO3r+T897axuI92zhvzzaWvf0arcePp/7tcrDrPTPYfNrZbJ51Fq/PPZc35i/i+eHJiR5b5ZoEVHKAcF0bAe66dikQHQXuOq/unl5uf3hL5Gw4/L0oIZJrFdZ+4oLYSUkpRDlTRDkVzGlvY9eh/rIEhgbHibiSsHeXUMa2GjSKUHgvcD8wD9gBfFJVY/1CixEK5Rgck1YOtZgZJJ1Xubwgks497cOf5ncLx1Jl9uG3C6sTf6Vy2rsJbsNlpj83kbaL3w8XXgjLlsFFF8GiRTAunRY2HPEN5V1xxg1S7W05Z82JpGsRNxHwVTFRx20Bpk7KFR1YCHlvJ2SkK6rfZ+COC6jUqiQ4Trj6pb0tx8Zbo5Nj1gt1533ko6o/Ie9lhKruB0Y76ZeZchh+4oxqEJ1fCCobXZl0XuXSbSbZS9LqZtP8buFYIuyeMpPdU2by2DkXFz5PWgX5nHL8KL+y97W8UNmzjcVvbWPR29mrzbYNHIN//uf8XxF8dsY8Lpx1VsFA/8tTF/Auk8oWjRpnB4izvxRbLyJcWyTMMOliIYLkWoXJ48eNKMUJ7vutHJHmWUhT5c2Pi2lUmi6iuRyDY5xRLWtUbLlWFUnnVU7jd5xhNY2HS4ukE5BJxzpyfJDunt7E/d6dMIln557Hs3PPA7LNjFuHh1iw/00W732NDx58nWtb90FPDxw+nNj+MAvf3sHCt3dw9ZYnR3+YFP04e3Z+dXLhhfkVyrJlMG/eiFQsfpBf1ll50oSoXGVVk2iVk+qmMFnu00p5N4XHiWKeqTTPe61tEDVVH5VKscFrpXZ6nCrG5S4ZpZ4qtwG71jeT34Y0S/fXE1R1cfrsIG25Vq65qIMHNvSOjFb2quGFiyql6d+yXpfdu6Gnh6/d+b9ZvGcbi/e8xunvVCe40ed46zjPbThvS9ly2lm8PGMeA625RFUMlD9Q0UVW+5YL1/M5vlUYGqag8rp4wTSe23Eo1Xn5NrNSnqc091W1nFrq1qZQKtWKaA4Td+FcOY2iZqfljnasNcG02SLuxGZJ5xele48jrtxosTMzKG+AUJIROPLYR47Apk2wYQM891x+hdLTU3Qbiubcc3nzzF/hhy2z+Jcp89gy+2wOTjilqEP5JU6Lve/TXs8oz6moaOwR9yzxzgalxp6ked6rNSbUrU2hUUlaNqZVT1UzB3sWillxRGW6dD1kwULrUcfJmm11V1+/U6WVFEPgiki98+olZX0Io9R7voujs32TJ8PFF+f/MrLgiw9zxsHdnPfWqwVvr/P2bKP92LvZG//yy5z+8st8Hvh8xq8eaJvC5llnsfm0s9hy6lmc9dEPsWD5+dz895szq3DTRk13LeuIdNGOisYO3h8ut1WfUu2DaZ73ehgTTCikJGqgjBo0wgLDz/N/07qNrH1k64gBtlo52LNQbAKvuDrUYVwF2P3jlFsnHofLBnTbQ6Vn2AxSjfQEQWZPm8x26WD79A4eft9vjPgsPOONMtRPP3qI8/Zs439dOP7kSmXbtsztmN7/Dh96vYcPve6tcB76/4B8VPwo/ISRM2YUsg8HMxH/70f3MMB7oPXksOVKY+IyrMcNrnH3Zfj3KpXMrh7GBBMKKSg2W2LS9+ox2rHYfO1ZZjJx+8Z9Nm1SjmMDw2XtL9fv9fWXlmEzimpGPrvurSjddNRAdGDSVLYtWwFfLGK1dOwYV/6//5337dnGkj2vFmwpueGUNom3387/PfvsiM33x33n5rygOTZuPDunzmLn1Fle9uFZvNk+q5CJeOqs9zoPkfYejorSTiPo0zzv9TAmmFBIQbEDZdL3Spk9VsqoXOzyNYuHStysx3UcAW79eN7Vr5znHdfuRqyv65Pl3ir7QDRxIjsWvI8XZp/D90IfJfrwq+ZT0oeTRG7fzq6eXzLn4FvxPz14gnP27+Sc/TvdOzk8vbYDve+ZmRciXvGtoHDZd8o0VFoK928xk0WIvybVXlFGYUIhBcUOlK7Pg4NQGp132lrL/vFKodjl66qVC1OnQ57/Xvex0ujeiz3HqL5ctXJhXdb8LQdpVyaVGIhCRQsTt4/YYdas/F/IlvKMw8Ej+H7SiX5OP7SHuYf2MK/vLX6lfz/T9vay4PBeznhnD+P6j8b+fMfhfXQc3sfynZvj2+mtTLoCm/omnpIXHt0d8FvLR6q/zjgDJkxIdU1qnUvLhEIK4kpErljzhPNBmtqWc+o206gmstZaLsfMtqRZY9ID7/GLbQec51/O1VMwFUn7pBzvHhssCK2gQXnapFxiVbaxTrkHImet7IzBbEFc90bQ4+/o+DZenjmfl2fOp6O9jVsj7H4uD5957xnPnyydwo+6f8bE3h3M69vD3ENvMbcvL2ROPRJf86v92Lt5Q/6ebbD5p9lOLpcbZUMZ8XratGzHKwETCimIGihzLcKREyczdUbN1geG3Oms0wzgWWstl2NmW2xATpb6Cko+P1LY8B4e1J2umo42hAVo0JMkapDyBemtH19ccz1uWuohFiWJ7p5ep+dZWNBmPR+X8Mpy/VzPyc7DJ1h5xXJWXrGcFWue4PtZ3EdVaT92mHl9b3HBwEH+9ILJo2ulDMektx8YgJdfzv+l5Z/+CS4vfxIIEwopiBooj54YHDXQhGfrR064DWtpBvCsg3y5ZrZZZo3+YFxMwZ2ksqJZVGLFpjbw3Vn9Y9T7YFuLFCpZcXmQCSPTm5TrfLJevzQq0rjSrJGxDSL0tU3h+JRp/O7VSyDr9ejvP1l4K6r41jvvjP7O/uwp79NgwWtFkiax3vyYQiWQHAzjWua6vHCqmcrbpxzZL+MCmvzPw/0UnmEW24as2XFrKSwaJdgxLhdVMJK9VueTJqGlK11Ie1uO44PDo2xeSunBbdXEgtcqQJrZRnuMTQHyg+Cq7z/P7Q9voe/owKhBx6Xfr4QXThxxA2M5cuIkrYjCM8ioGWZSNGoU4ZlrmHqbmddDYFMaXM9GR2glW+r5FCuwk1YWcSsdkdEFm3yBUE+CuRRMKBRJGoPsxy6YHRshCfkoS18NFR50km7eagxMcQPj+jfKk7p6TsJKAUaq5lyBclkFg0Ih8CmqL4t1Ra4U9RDYlIa0zgqlnE+pAjtOReoSSorbUF5vgrkUTCgUSRo95o9f2J35uOFBJ41+v9wqjuDxogq49w8MFVY3pSLk0150njE9Melab19/rLqqGEVo3GCSpcRkNVZs9RDYlIa0Ov5SzqeSAjtppdMIgrkUTCikwPXgJw3YWXPJ+2SZdZRbxRE+nsuAXOy5hVHggQ29dJ4xPTahoE+cqmhakQVdXINJ0ky22uqlWhjES1HRRO0XPt41F3VkrmDY3dMbawgulSRh1QiCuRRMKCRQC71yuQr+FNO+ShYogWgVj59raPKEcezq62daKKYgTJSqqC3XimpxqwWIHkySBodaqJeqGdhU6QlHb18/D2zozeQg4R/DRTlm7GmEb704H1QCEwoJlPLguwzNfozX1LYcR04Mjio1WK6CP8VQCd1o0DsjrjKY31cHjw6Qa5VYQ71/vOCDeZMjMjkNUYNJ3OBQ6dlqPZDm3s+ykiiHEI2btJRzxh4nfGsdcVxpTCgkkCZVhYvbrlw8KvVDOKd7qTrpchsfXcdz1eF10SrCsOqoc0rrwjowpEyeMI7JE8Y52xPusyTVk4tcqzgHk+AA4F+rG9dtjA3ejuv7enJxTSJpwpF1JVGOCUzcvrVwyR6LtNS6AfWO6wEX8g9FHF3LOlj7yQvoaG9DyM9sw0U+upZ18PPVl7F9zRWFkP0zV/+YFWueSDw+5FUcbbnWEdtKmTFdumjmqAGvLdfKVz91QdosFgAMq7J9zRX8fPVlI843qr0uevv6nfsPqaLePjeu28jS2x/l0kUz84XeMzJ5/LjUKUd8oeMSj3F9HzyG3/abH9yU6jrXAte93yJSEG5xtcrTHk8h9f3uOkZHe5sJhDJhQiGBVSsXRg6GQXfGOIKDfniADFLsgNG1rIM7r14yQvCUUs7zgQ29IwY8Aa65KD9bzrL6cO3rt7e9LZd4DL/fg+fX6sio1tc/wAMbesm1ZhcKh2JiSXzS2lri+j7rIFpr4gRyUECGcc3m4yYEae/3ck+CjNGY+iiBrmUdVcmiWYq+NUrFcdO6jYUCP4f6RwfGpW2DcrL4SJTh1UX4IQ2rTRKzZXJS8PrCtLun13ktYHRQUVrSCLs0aqn2tlyh76P6u1GCz3z8tkflteofGHKqFOMmBIBTzecqmBMVm9MoKrhGxIRCClwG0nL6JpdjwAjreINuo3H63mAul7g2hB/IqBgGyA+O4ZQUYd1zWsL663KTtgxkUmBcrkU4fHxkgsRVP3geONlvjRJ8FqRrWYfTgD+kOip1dVJ/+hMYVyoM/z6Ns1OMdUNvrTH1UQoqvWTt7umlxTF1LtU9NUiUqiKsK09qQ1Ad9tVPXRDZL7dduThTu+KYmGvJdIzJ45PtFa0iI1RtkNdpu2w5tz20JbGg+/hxLQyFXGgHhpTbH95SeN+oqo+4e/DY4MlrMm1SLrXq0nXMVpGGUrGNRUwopKCcevswcVlGy+WeGsSPCvYHvqTBNq4NafulFPXI8cHhTMfItbYwbVK8vWIo4BUFxNpyunt6Y/NX3X3tUn6++jJnRtzgaq1S91F3T2+sUCuVOFtA8LY9NhCTGjrFMdtyrU4Pt3pVsY1FTH2UknIvWZNUNq0imQeMtNlCg0vyuIetIzBwuooJRfVL2H4QV2woCX/ynfbcDvUPcNe1S2NtD5C+YFHSDDWrSqsS91GlgyuTbAE+WWIYXLYB12/Us4ptrGErhRqQRmUzrJr5oc7i7uk/wHEufn7WxyxeUVFeVEdODI5yFc3iI9Td05v63Hzj9KRc8q3dPzDkFFa+sEyaofr96PKmSuNlVQrV8mjy1YZJ183vr1u6N3HTuo2x902UZ16jqtjGElUXCiIyV0SeFJEXRWSLiPyBt326iDwmIq94/6tXf67KpNGPFzMzCqsnpk3KxQ5KuxxxAGlTOUQRtf/AkHLKxHEj1CZZ0lH4M9/gucXZDnr7+hkY0qLcU338/k9zHXb19XPblYtHCb5ci4yyr5Sbans0JfXHnPY2unt6ufepHZHpTJKEVSVVtUY6aqE+GgT+UFWfE5H3ABtE5DHgc8DjqrpGRFYDq4Ev1qB9FSfpgS1lZhSlnnBFEc8JBPy4lvlZBx3X9r6jA/R8+SMFlUIW/MEk6JqapLYZGFba23IcPjaYuSpcsM5CGjfcNP1YKart0RTXH/5966pHAOmElXkX1ZaqCwVV3Q3s9l4fFpEXgQ7gKuASb7d7gJ8wRoVCnH68EtWbkhK7xT2EWQeduP1v6d4UOYMMtilN/em0nkhp7QthFCJ131EZWtP2Y6Wodjptl32hVYRrLkouvGS2gfqnpjYFEZkPLAOeBmZ5AsMXHKfWsGkVxaWy8T1Zyj2wlLIkz6rjde0//71tfDdGIPjujOHqXD7BwSStamSqFy/h8kZyKZfCbfB136+vuYK7rl1aV6qNWqhbonT/Q6qse3Ynq77/fOx3jxwfrNu0HkaemnkficgpwAPAjar6jqQJcc1/7wbgBoB58+ZVroEVpBaqhmJnsVnbGrX/pYtmcm9CBbpJgfxDSTPftJ5IR07kB6BbP7448pjXXNTBAxt6iwq+qidq0SaX7SiJvv6BmpY0NZIRzahvLcuPiuSAHwGPqOrXvG1bgUtUdbeIzAZ+oqqxa+DOzk5dv3595RtslESazKgCbPeKuidlEo0qvO7C96JyHbORspZCcVlWK3GOrojktIylmsaNiIhsUNXOqM+qvlKQ/JLg28CLvkDweAi4Hljj/f9htdtmVIY06p5w1HTcoJXWbz74265j1uPM30UxMQmVimNIu1pzYcFo9UstbAorgM8Cl4nIRu/vt8kLg98UkVeA3/TeG2OAJONiMYbRtH7zY8mwWUxMQqXiGLLExEQxlq7LWKMW3kc/w23ju7yabTGqQ5wbY6neVnHR0kHX0rFAMTEJlYpjyLJaCyPk63YY9YlFNBsVp2tZB9dc1BE5E5j/3tKKo8T5J3zm4nkNoxpKQ1LRmzDlSrTowl+t3X3t0lGrhrgVnAIPbOg1L6Q6xYSCURWefGlfpGHy59sOcEt3tvxBwQRwwYRzYe7oWhL5nUokjasGSUVvgudUzkSLSUS5xX7m4nmx6iXLfFq/WEI8oyrEqSvue3rniAE8jrSeR8FYg2okjSsHSV5CSUVvgsnoXAF+xSRaTEOUwb7zjOmp6nQY9YWtFIyqEKeuyJKGIk00c3gm3AhlMKMSCfq1p8NJ5IZTpJd2DbjFJFosFl+95EpOaMbm+sSEglEV4tQVrrrLUcQZNV0RvY1QBtMl7Pxgr6BgiEsxkmWftJSierulexNHI+ostMjYcgIYS5hQMGrOdcvnptrPL4sZRasIdznShJRzgKwUcQIqvKpJk3qkXCmoo1YwcanTw9z39M7I7cPVj5k1UmJCwagKLlVNW64ltT0hLvtmlLHVpxFy9CcJqKDQSJPvqFw5kbKo3qJWFHGqwSzCxageZmg2qoJrJpylhGPagjcwOlfTnVcvqet0FkkpusNCI00kdjmitdOq3lzGfJGRJTuDhI3jRn1gQsGoCuXI+98+KRfrggonB6Pw4HTn1UvqOteOPzDe/vCWUedYy1VN2uvmWlFMyrVE2hR86smuY+Qx9ZFRFcqhwknjpNQqUveeRi66lnXQ8+WPcHcdpedOe91cg3v/wDC/c7E7m3E92XWMPLZSMKpCOdKFH3Kks/BJW6Sn3qmnJH1pr1vciuKOriV0njG9qsWAjOIxoWBUjVIHuzQV61zBUjYjLZ401y1NdT+ofrlSIzsmFIyGwTXwhNUrNiOtPmkG/XpaARluTCgYDUPagSdpH6My2KA/NqhJ5bVyYZXXxhaNVgXNMBqVuqq8ZhhRNErSOsMY65hLqlEXNELSOsNoBkwoGHVBIyStM4xmwISCURc0QtI6w2gGTCgYdUEjJK0zjGbADM1GXWCupIZRH5hQMOoG83M3jNpj6iPDMAyjgAkFwzAMo4AJBcMwDKOACQXDMAyjgAkFwzAMo0BDJ8QTkX3AGyUcYgbwdpmaM1awPonG+mU01ifRNEK/nKGqM6M+aGihUCoist6VKbBZsT6JxvplNNYn0TR6v5j6yDAMwyhgQsEwDMMo0OxC4Zu1bkAdYn0SjfXLaKxPomnofmlqm4JhGIYxkmZfKRiGYRgBTCgYhmEYBZpSKIjIR0Vkq4i8KiKra92eWiEi3xGRvSKyObBtuog8JiKveP+n1bKN1UZE5orIkyLyoohsEZE/8LY3bb+IyEQReUZEnvf65HZve9P2SRARaRWRHhH5kfe+oful6YSCiLQC3wB+C3gfcJ2IvK+2raoZfwt8NLRtNfC4qp4DPO69byYGgT9U1V8BLgY+790fzdwvx4HLVPUCYCnwURG5mObukyB/ALwYeN/Q/dJ0QgH4APCqqr6mqieA7wFX1bhNNUFVfwocCG2+CrjHe30P0FXNNtUaVd2tqs95rw+Tf9g7aOJ+0Tzvem9z3p/SxH3iIyKnA1cA3wpsbuh+aUah0AHsDLx/09tm5JmlqrshP0ACp9a4PTVDROYDy4CnafJ+8VQkG4G9wGOq2vR94nE38MfAcGBbQ/dLMwoFidhmfrnGCETkFOAB4EZVfafW7ak1qjqkqkuB04EPiMh5NW5SzRGRjwF7VXVDrdtSTppRKLwJzA28Px3YVaO21CN7RGQ2gPd/b43bU3VEJEdeINyrqg96m5u+XwBUtQ/4CXlbVLP3yQrgShF5nbwa+jIR+S4N3i/NKBSeBc4RkTNFZDzwaeChGrepnngIuN57fT3wwxq2peqIiADfBl5U1a8FPmrafhGRmSLS7r1uAz4MvEQT9wmAqt6sqqer6nzy48gTqvo7NHi/NGVEs4j8NnldYCvwHVX9Sm1bVBtE5D7gEvKpfvcAtwLdwP3APGAH8ElVDRujxywi8mvAPwObOKkn/hJ5u0JT9ouInE/eYNpKfiJ5v6r+FxF5L03aJ2FE5BLgj1T1Y43eL00pFAzDMIxomlF9ZBiGYTgwoWAYhmEUMKFgGIZhFDChYBiGYRQwoWAYhmEUMKFgNCQi8m7o/edE5K9q1JZxIvK2iNxZhmMtE5FvJe9Z0m/MFJF/rORvGI2LCQXDKJ2PAFuBT3nBb6XwJeDrpTcpGhEZp6r7gN0isqJSv2M0LiYUjDGHiJwhIo+LyAve/3ne9r8VkU8E9nvX+z9bRH4qIhtFZLOI/Lq3/SMi8i8i8pyIfN/LhxTFdcBfkA9Uujhw/N8WkZdE5Gci8peBfPuTvVoWz3p5+K/ytr8HOF9VnxeRFi8f/0zvsxav/scMb6b/gPf9Z/3BXUQ+ICK/8I75CxFZ6G3/nNf+h4FHveZ1A58pS4cbYwoTCkaj0uYN4hu97J3/JfDZXwH/S1XPB+4F/jLhWP8aeMRL+HYBsFFEZgC3AB9W1QuB9cAXwl/00j5cDvwIuI+8gEBEJgL/A/gtVf01YGbga39CPiXC+4FLgbUiMhnoBDYDqOow8F1ODtwfBp5X1bfJC6C7vO9fw8m0zS8BH1LVZcCXgT8L/OYHgetV9TLv/Xrg1xP6xWhCxtW6AYZRJP3eIA7kZ8PkB1XID4BXe6//DvjzhGM9C3zHS4TXraobReQ3yBdh+rmnERoP/EvEdz8GPKmqR0XkAeA/i8hNwCLgNVXd7u13H3CD9/oj5BOp/ZH3fiL5lAizgX2BY3+HfN6cu4HfBf7G2/5h4H0BTdUUb5UxFbhHRM4hn/k3FzjWY6FUC3uBOQn9YjQhJhSMZsDP5TKItzr2dP/jIV9sSEQ+RL5Yyt+JyFrgIPmB9LqEY18HrPAyZQK8l/zsf3/MdwS4RlW3jtgosoi8gMBr104R2SMilwHLOblqaAE+qKr9oe9/nbyA+ldeLYifBD4+EmrDRKAfwwhh6iNjLPIL8lkrIT+Q/sx7/Tpwkff6KryZtIicQT4v/v8knyH1QuAp8oP92d4+k0Tk3OCPiMgU4NeAeao638uW+XnyguIlYIE3OANcG/jqI8B/8o3SIrLM2/4icHboXL5FXo10v6oOedseBf5joB1LvZdTgV7v9eeiOibAuXiqKsMIYkLBGIv8PvBvReQF4LPka+gC/E/gN0TkGfIzb3/2fAl5O0IPeR39X3geOp8D7vOO8xR5lVCQq8nbBo4Htv0QuJJ8htX/APyjiPyMfBbaQ94+f0peIL0gIpu996jqS8BUTxXk8xBwCidVR/75dXqG9F8Cv+dt/3PgThH5OfmMpnFcCvw4YR+jCbEsqYZRIUTkFFV911sRfAN4RVXvSvjOTcBhVf2W976TvFG5rEZhEfkpcJWqHizncY3Gx1YKhlE5/r3nGbWFvGrnf6T4zn8DjgOIyGryFeBuLmejPDfXr5lAMKKwlYJhGIZRwFYKhmEYRgETCoZhGEYBEwqGYRhGARMKhmEYRgETCoZhGEaB/x90XyoMuCAewQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "import numpy as np\n", + "PredictedPrice=reg.predict(Age)\n", + "\n", + "fig = plt.figure()\n", + "plt.scatter(Age,Price)\n", + "plt.xlabel('House Age(year)')\n", + "plt.ylabel('Unit Price')\n", + "\n", + "plt.plot(Age,PredictedPrice,'r')\n", + "#plt.legend(['Regression Predicted','Real Prices'])\n", + "reg.score(Age,Price)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wh2QOfSdbR9t" + }, + "source": [ + "# Extra Material for option 3 \n", + "\n", + "Correlation Matrix: It measures the relationship between two variables. The measure is best used in variables that demonstrate a linear relationship between each other. \n", + "\n", + "-1 indicates a perfectly negative linear correlation between two variables\n", + "\n", + "0 indicates no linear correlation between two variables\n", + "\n", + "1 indicates a perfectly positive linear correlation between two variables\n", + "\n", + "Code For Option 3 " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "\n", + "sns.heatmap(HouseData.corr(),annot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "executionInfo": { + "elapsed": 21, + "status": "aborted", + "timestamp": 1624863836155, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "suMBL-mobR9t" + }, + "outputs": [], + "source": [ + "## Start your Code from there and create as many as cell you need to make your code easy to follow ##\n", + "#Ill create new cells" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wi1FaLr4bR9t" + }, + "source": [ + "# Feedback\n", + "\n", + "We appreciate feedbacks from you to make the exercise better for future generations. \n", + "\n", + "1) What do you find most useful in this document?\n", + "\n", + "2) Is there any part that was not clear to you?\n", + "\n", + "3) How do you rank your coding and regression skills before the task and how difficult was to follow this document?\n", + "\n", + "4) Any other suggestions to make it more fun?\n" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "LinearRegression.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/task0/Python_Intro_soner.ipynb b/task0/Python_Intro_soner.ipynb new file mode 100644 index 0000000..98bd3f7 --- /dev/null +++ b/task0/Python_Intro_soner.ipynb @@ -0,0 +1,2455 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "FQJwIvXEgjRN" + }, + "source": [ + "# Basics of Python, Numpy\n", + "\n", + "\n", + "Ali Alp Gurer- aalp.gurer@gmail.com\n", + "\n", + "This is a great source to learn basics for python or review the content.\n", + "\n", + "The best practice is to create new cells from the + above and write down a similar exmaple on your own.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "upeBF1ErgjRQ" + }, + "source": [ + "## Installation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bIVzGU7pgjRS" + }, + "source": [ + "https://www.anaconda.com/distribution, Check out the welcome email for more detailed infomation. If you are able to open this document, there should not be any problem." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nE-OEgcJgjRT" + }, + "source": [ + "## Python basics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5B6cCnq-gjRV" + }, + "source": [ + "### Writing a comment" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "sD5qlqtdgjRV" + }, + "outputs": [], + "source": [ + "# This is how you comment things out. Type '#' followed by your text" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "M3ae-P80gjRW" + }, + "outputs": [], + "source": [ + "# Here is another example of a comment" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "J3z_pHg4gjRX" + }, + "outputs": [], + "source": [ + "# Now lets use comments" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "W_z3yoXlgjRY", + "outputId": "d5e7afcd-8754-4e2d-ccb2-b23389a2ad77", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n", + "-1\n", + "2\n", + "0.5\n", + "1\n", + "3\n", + "8\n" + ] + } + ], + "source": [ + "print(1 + 2) # Addition\n", + "print(1 - 2) # Subtraction\n", + "print(1 * 2) # Multiplication\n", + "print(1 / 2) # Division\n", + "print(10 % 3) # return the remainder\n", + "print(10 // 3) # return the quotient\n", + "print(2 ** 3) # number raised to the power" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s7Q3OrNFgjRZ" + }, + "source": [ + "### Type and type conversion" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "B8UreVwMgjRa", + "outputId": "ae7430cd-6cf2-432d-d63d-8abc44a6bfe3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(type(9))\n", + "print(type(9.0))\n", + "print(type(1+3.33))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TjpCKaSfgjRb" + }, + "source": [ + "### Variables " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ehmf3ZijgjRc" + }, + "source": [ + "- Varialbes are assigned with the variable name on the left, and the value on the right of the equals sign. \n", + "- Variables must be assigned before they can be used.\n", + "- Can be reassigned any time\n", + "- Variable name must start with a letter or underscore, cannot start with a number. Case sensitive.\n", + "- Wrong examples: 2cats, hey@you" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iD2wENB7gjRc", + "outputId": "06601bfe-018b-41c1-a983-eb845a9f7dfb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30\n", + "200\n", + "30\n", + "30 40\n" + ] + } + ], + "source": [ + "num_1 = 10\n", + "num_2 = 20\n", + "print(num_1 + num_2)\n", + "print(num_1 * num_2)\n", + "num_2 = 30\n", + "print(num_2)\n", + "Num_2 = 40\n", + "print(num_2, Num_2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4kae_nFggjRd" + }, + "source": [ + "### Data Types" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SDF3WTAsgjRe" + }, + "source": [ + "- bool : True or False\n", + "- int: an integer\n", + "- str: a sequence of Unicode characters, like \"friday\"\n", + "- list: an ordered sequence of values of other data types\n", + "- dict: a collection of key & values" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mV2UKXPigjRe", + "outputId": "547589f1-afc2-4c14-cfa1-698ebfdc84e9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n", + "True\n" + ] + } + ], + "source": [ + "print(1 != 2) # not equal\n", + "print(3 != 3)\n", + "print(4 == 4) # equal to" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S50444cpgjRf" + }, + "source": [ + "- \"==\" operator compares the values and checks for value equality. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "8yStd7mCgjRg" + }, + "outputs": [], + "source": [ + "day = 'Friday' # string\n", + "is_it_friday = True # bool" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hP5sbZPrgjRg", + "outputId": "f316282e-f157-4c5f-e3ae-b456a2dbb602" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'Friday' == day" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "izzWdiKwgjRh" + }, + "source": [ + "### Length" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rcsFP8G0gjRh", + "outputId": "0a6e5a66-e96d-47d7-cd1d-d6c2288d3e09" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6\n" + ] + } + ], + "source": [ + "print(len(day)) # There are 6 characters in the word 'Friday'" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sNeLsnuxgjRi", + "outputId": "220f2d7b-2de0-42f1-e737-16b5e0b336c8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n" + ] + } + ], + "source": [ + "print(len('day'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v43072TEgjRj" + }, + "source": [ + "### Length of a sentence" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RoKGQdGvgjRj", + "outputId": "7af3b2ee-2824-49ad-d077-e07f009c91ba" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18\n" + ] + } + ], + "source": [ + "print(len('Ah, Welcome class!')) # space, symbols, numbers are counted as characters" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EEZu8eaWgjRk" + }, + "source": [ + "### Strings\n", + "\n", + "- can be declared with either single or double quotes" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BYqrQwbQgjRk", + "outputId": "665ddcb7-9b47-49cf-9898-7ea75dcc40e5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "city : PIT\n", + "10\n", + "t\n", + "[' ', ' ', ':', 'I', 'P', 'T', 'c', 'i', 't', 'y']\n", + "['y', 't', 'i', 'c', 'T', 'P', 'I', ':', ' ', ' ']\n", + "DL for Engineers: 24789 ! \n", + "DL for Engineers: 24789 !\n", + "DL for Engineers: 24789 !\n" + ] + } + ], + "source": [ + "txt = \"I need more and more holidays\" \n", + "# count method\n", + "x = txt.count(\"holidays\") # returns a number equal to the number of occurrences of the word entered\n", + "y = txt.count(\"more\")\n", + "print(x)\n", + "print(y)\n", + "# String concatenation\n", + "str_one = \"city\"\n", + "str_two = \"PIT\"\n", + "str_all = str_one + \" : \" + str_two\n", + "print(str_all)\n", + "print(len(str_all))\n", + "print(str_all[2])\n", + "\n", + "# Operations on string\n", + "str_all_sorted = sorted(str_all)\n", + "print(str_all_sorted)\n", + "# Here is an example of sorting in the reverse direction\n", + "str_all_sorted_reverse = sorted(str_all, reverse = True)\n", + "print(str_all_sorted_reverse)\n", + "\n", + "# Formatting\n", + "course_number = 24789\n", + "formatted = f\"DL for Engineers: {course_number} ! \" # Python 3.6+\n", + "formatted2 = \"DL for Engineers: {} !\".format(course_number) # Python 2 -> 3.5\n", + "print(formatted)\n", + "print(formatted2)\n", + "# Another way of printing the same sentence\n", + "print(\"DL for Engineers:\",course_number, '!')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NcXc1UfVgjRl" + }, + "source": [ + "# Lists" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-cSTOfCjgjRm", + "outputId": "cc417071-5d91-45cf-cd08-1693acc00b7b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "['aaa', 'bbb', 'ccc', 'ddd']\n", + "['aaa', 'bbb', 'ccc', 'ddd', 'More objects', ['List inside a list!']]\n", + "\n", + "Length before adding another_list 4 \n", + "\n", + "Length after adding another_list 6 \n", + "\n", + "['aaa', 'bbb', 'ccc', 'ddd', 'More objects', ['List inside a list!'], 'More objects', ['List inside a list!']]\n" + ] + } + ], + "source": [ + "# Lists - can store anything\n", + "list_of_things = [] # Initializing an empty list\n", + "\n", + "### \n", + "list_of_things.append('aaa')\n", + "list_of_things.append('bbb')\n", + "list_of_things.append('ccc')\n", + "list_of_things.append('ddd')\n", + "###\n", + "\n", + "# Accessing objects\n", + "print(type(list_of_things[3]))\n", + "\n", + "# Print the list\n", + "print(list_of_things)\n", + "\n", + "# Create another list\n", + "another_list = ['More objects', ['List inside a list!']]\n", + "\n", + "# Merge the lists\n", + "third_lists = list_of_things + another_list\n", + "print(third_lists)\n", + "\n", + "# Extend list_of_things\n", + "print('\\nLength before adding another_list',len(list_of_things),'\\n')\n", + "list_of_things.extend(another_list)\n", + "print('Length after adding another_list',len(list_of_things),'\\n')\n", + "\n", + "list_of_things+=another_list # list_of_things = list_of_things + another_list\n", + "print(list_of_things)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tWQPGMC4gjRn" + }, + "source": [ + "### List slicing and join" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vv9SFjyggjRn" + }, + "source": [ + "#### some_list[start : end : step]: like range but separated by colons" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eSOvQgFcgjRo", + "outputId": "75444041-5cfb-41b5-a1a4-241f240be1f8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6\n", + "red\n", + "['red', 'blue', 'green', 'yellow', 'purple']\n", + "['red', 'blue', 'green', 'yellow', 'purple']\n", + "['green', 'yellow', 'purple']\n", + "['green', 'yellow', 'purple', 'indigo']\n", + "['red', 'green', 'purple']\n", + "['indigo', 'purple', 'yellow', 'green', 'blue', 'red']\n", + "ogidni\n", + "Python.Is.Great\n" + ] + } + ], + "source": [ + "colors = ['red','blue','green','yellow','purple','indigo']\n", + "print(len(colors))\n", + "# Python indices start at 0\n", + "print(colors[0])\n", + "print(colors[:5])\n", + "print(colors[0:5])\n", + "print(colors[2:-1])\n", + "print(colors[2:])\n", + "print(colors[::2])\n", + "print(colors[::-1])\n", + "print(colors[5][::-1])\n", + "\n", + "# Join (a string method)\n", + "words = [\"Python\",\"Is\",\"Great\"]\n", + "words =\".\".join(words)\n", + "print(words)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g9SSqzGdgjRo" + }, + "source": [ + "### Boolean and condition" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_IU_sD1jgjRq" + }, + "source": [ + "### if elif else\n", + "change the color to purple and red and run the code again" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "id": "fTVK50KjgjRq", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Thats new!\n" + ] + } + ], + "source": [ + "color = 'yellow'\n", + "if color == \"purple\":\n", + " print(\"great choice\")\n", + "elif color == \"red\":\n", + " print(\"I like how you think\")\n", + "elif color == \"pink\":\n", + " print(\"not bad\")\n", + "else:\n", + " print('Thats new!')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lAYNYjWDgjRp" + }, + "source": [ + "# Exercise:\n", + "- age < 18 : cannot vote\n", + "- age => 18 : can vote\n", + "\n", + "Code the above example, if age is less than 18, print cannot vote, otherwise print can vote " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BHP3_m-CgjRp", + "outputId": "7c9807df-fe0d-4149-ac29-f61ea23e4b2e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19\n", + "can vote\n" + ] + } + ], + "source": [ + "age = int(input())\n", + "if age < 18 :\n", + " print(\"cannot vote\")\n", + "else:\n", + " print(\"can vote\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ocTacuqLgjRr" + }, + "source": [ + "### Looping" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BDihTqROgjRs" + }, + "source": [ + "#### General Form :\n", + "#### for item in iterable_object:\n", + " do something with item\n", + " \n", + "- item is a new variable that you can call it whatever\n", + "- item references the current position of our iterator within the iterable" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MEeyZlhHgjRs" + }, + "source": [ + "### Ranges" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xXN-CHm3gjRt" + }, + "source": [ + "- A range is just a slice of number line.\n", + "- Ex: range (1,8) will give you integers from 1 to 7" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_4dF6lrkgjRt", + "outputId": "5ff8ba75-034a-4be9-a753-6374370b529a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n", + "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n", + "[1, 3, 5, 7, 9]\n", + "[0, 2, 4, 6, 8]\n" + ] + } + ], + "source": [ + "numbers = range(10)\n", + "print(list(numbers)) # It starts with zero and excludes the number 10\n", + "numbers = range(1,10)\n", + "print(list(numbers)) # It starts with one and excludes the number 10\n", + "odd = range(1,10,2) # start, end, step\n", + "print(list(odd)) #It starts with 1 and takes a step of 2\n", + "even = range(0,10,2) # start, end, step\n", + "print(list(even)) #It starts with 0 and takes a step of 2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bg5tQyHfgjRu" + }, + "source": [ + "# Exercise \n", + "Use a for loop to add up every odd number from 10 to 20 (inclusive)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "k6j0VqPQgjRv", + "outputId": "df06294a-4298-45e2-8535-b49fed130e8c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sum of the numbers: 75\n" + ] + } + ], + "source": [ + "add = 0\n", + "\n", + "##Exercise: Use a for loop to add up every odd number from 10 to 20 (inclusive)\n", + "for i in range(10,20):\n", + " if i % 2 == 1:\n", + " add += i\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "##\n", + "print('Sum of the numbers: ',add) " + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "id": "IUCULCLGgjRw" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10, 11, 12, 13, 14, 15, 16, 17, 18, 19]\n", + "number is : 10\n", + "but i is : 0\n", + "number is : 11\n", + "but i is : 1\n", + "number is : 12\n", + "but i is : 2\n", + "number is : 13\n", + "but i is : 3\n", + "number is : 14\n", + "but i is : 4\n", + "number is : 15\n", + "but i is : 5\n", + "number is : 16\n", + "but i is : 6\n", + "number is : 17\n", + "but i is : 7\n", + "number is : 18\n", + "but i is : 8\n", + "number is : 19\n", + "but i is : 9\n" + ] + } + ], + "source": [ + "\n", + "numbers = range(10,20)\n", + "print(list(numbers)) # It starts with zero and excludes the number 10\n", + "i=0\n", + "while i\u001b[1;34m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcmu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcmu\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"founder\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcmu\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"mascot\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m: 'mascot'" + ] + } + ], + "source": [ + "cmu = {\n", + " 'founder': \"Andrew Carnegie\",\n", + " 'private school?': True,\n", + " 'acceptance rate':0.2,\n", + " 4:\"number of other campuses\"\n", + "}\n", + "print(cmu)\n", + "print(cmu[\"founder\"])\n", + "print(cmu[\"mascot\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LauX2yppgjR2" + }, + "source": [ + "### Accessing all values in a dictionary" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rwwEht89gjR3" + }, + "source": [ + "- Access value: Use .values()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "_pY-R0fogjR3", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Andrew Carnegie\n", + "True\n", + "0.2\n", + "number of other campuses\n" + ] + } + ], + "source": [ + "for value in cmu.values():\n", + " print(value)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CGZ4kw-mgjR4" + }, + "source": [ + "- Access key: Use .keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "kNZWAqGKgjR4", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "founder\n", + "private school?\n", + "acceptance rate\n", + "4\n" + ] + } + ], + "source": [ + "for key in cmu.keys():\n", + " print(key)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lKghv7hfgjR5" + }, + "source": [ + "- Access both: .items()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "BK8DXGJMgjR5", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "founder : Andrew Carnegie\n", + "private school? : True\n", + "acceptance rate : 0.2\n", + "4 : number of other campuses\n" + ] + } + ], + "source": [ + "for key,value in cmu.items():\n", + " print(key, \":\", value)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ElqxNxtpgjR6" + }, + "source": [ + "- Test if a key exists: Use \"in\"" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "ggIYOUYogjR7", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n" + ] + } + ], + "source": [ + "print(\"founder\" in cmu)\n", + "print(\"good sleep\" in cmu)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CmJ-oVWqgjR9" + }, + "source": [ + "### Dictionary Methods" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UZBkJ2IagjR-" + }, + "source": [ + "- pop: Takes a single arguement corresponding to a ket and removes that key-value pair from the dict. Returns the value corresponding to the key that was removed." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "8lfNiDnogjR_" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = dict(a=1,b=2,c=3)\n", + "d.pop(\"a\") # 1\n", + "d.pop(\"c\") # TypeError: pop expected at least 1 arguement" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5I6e7gacgjR_" + }, + "source": [ + "## Introductory Example: Implementing with Lists " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-9c9RVj_gjSA" + }, + "source": [ + "To know the difference between an array and a list please Google ;)\n", + "\n", + "This blog also provides a good explanation : https://medium.com/backticks-tildes/list-vs-array-python-data-type-40ac4f294551" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "mTHI9ySdgjSB" + }, + "outputs": [], + "source": [ + "# Inputs\n", + "# Now that we know what a list is lets use it to do a simple calculation\n", + "theta_1 = [[2, 1, 3],\n", + " [1, 2, 3],\n", + " [3, 1, 2]]\n", + "\n", + "x = [7, 8, 5]\n", + "theta_0 = [1, 1, 1]\n", + "\n", + "# Output\n", + "y = [0, 0, 0]\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise \n", + "Computation of y = theta_1*x + theta_0 hint: you can use two FOR LOOPs " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "##### Computation of y = theta_1*x + theta_0 IKI TANE FOR LOOP YARATILMASI LAZIM\n", + "\n", + "\n", + "problem çok kapalı..\n", + "\n", + "\n", + "\n", + "\n", + "#####" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vahB6dJ7gjSC" + }, + "source": [ + "# What is NumPy? " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "13UuoyNMgjSC" + }, + "source": [ + "If you have installed Anaconda correctly, Numpy should come with it. Just in case, if numpy is not installed please follow instructions at https://scipy.org/install.html" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aQebz_JNgjSD" + }, + "source": [ + "### NumPy-based Vectorized Implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "adhT4ppOgjSD" + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "q4FYHtwigjSE", + "outputId": "6d328bf7-971c-4795-8d30-a5f81abba03b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[2, 1, 3],\n", + " [1, 2, 3],\n", + " [3, 1, 2]]),\n", + " (3, 3))" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "theta_1 = np.array([[2, 1, 3],\n", + " [1, 2, 3],\n", + " [3, 1, 2]])\n", + "\n", + "theta_1, theta_1.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "GH243e3JgjSF" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(3,)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.array([7, 8, 5])\n", + "x.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "Y9kuWr7MgjSG" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 1., 1.])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "theta_0 = np.ones(3)\n", + "theta_0" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "firMwD23gjSH", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([37, 38, 39])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "theta_1.dot(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "M2KgDDYmgjSH" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([37, 38, 39])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.matmul(theta_1,x)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "un59WjQ-gjSI" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([38., 39., 40.])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.matmul(theta_1,x) + theta_0" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "hpSjnhRPgjSI" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([38., 39., 40.])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "theta_1@x + theta_0" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-sCRV3GKgjSJ" + }, + "source": [ + "## Multi-Dimensional Arrays" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "id": "MOTZJ0z8gjSJ", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[0.37580706, 0.70830426, 0.09153579, 0.52723929],\n", + " [0.19994589, 0.72078552, 0.34593445, 0.71610907]],\n", + "\n", + " [[0.98326848, 0.79048882, 0.74869516, 0.24444319],\n", + " [0.27469896, 0.61857448, 0.49285548, 0.07838635]],\n", + "\n", + " [[0.17880171, 0.96976208, 0.672392 , 0.98105011],\n", + " [0.77531117, 0.76854727, 0.02065681, 0.12993514]],\n", + "\n", + " [[0.14966477, 0.70880084, 0.31973711, 0.48653843],\n", + " [0.74483763, 0.84677488, 0.00389611, 0.69743766]]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "T = np.random.random((4,2,4)) # Creates an array of random numbers between 0 and 1 of the given shape\n", + "T" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yvuRtYBRgjSK" + }, + "source": [ + "### Random seed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BsfaiLwzgjSK" + }, + "outputs": [], + "source": [ + "T = np.random.random((2,2)) # Creates an array of random numbers between 0 and 1 of the given shape\n", + "print(T)\n", + "\n", + "np.random.seed(24878)\n", + "T = np.random.random((2,2)) # Now, all the random numbers are fixed as per the seed\n", + "print(T)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QEGCtgmsgjSL" + }, + "outputs": [], + "source": [ + "np.random.seed(24878)\n", + "T = np.random.random((2,2))\n", + "print(T)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nOXicV7sgjSL" + }, + "outputs": [], + "source": [ + "W = T[1]\n", + "W.shape, W.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "I4j69S2cgjSM" + }, + "outputs": [], + "source": [ + "X = np.full((4,3), 24787, dtype=np.int32) # prints out the same in all dimension # remember it is a matrix multiplication for 2D \n", + "X.shape, X.dtype,X" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3d_urC50gjSM" + }, + "source": [ + "## Basic Element-wise Operations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JYbNt6qRgjSN", + "outputId": "5024cd93-48bd-4160-ed91-463e59325b88", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 2 3 4] [0 1 2 3 4]\n" + ] + } + ], + "source": [ + "y = np.arange(5)\n", + "print(y, np.array(range(5)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ODNjVMg5gjSN", + "outputId": "c72fde73-27ce-4e1d-96f9-b13b63b2640d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, True, True, True, True])" + ] + }, + "execution_count": 30, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "y == np.array(range(5))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "urb0W6GCgjSO", + "outputId": "ccb45bbf-355c-4ee9-e710-7ce94adaf8c1" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5])" + ] + }, + "execution_count": 32, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "y + 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VJh_OHn5gjSO" + }, + "outputs": [], + "source": [ + "y * 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9b1Cua2sgjSP" + }, + "outputs": [], + "source": [ + "(y + 1)**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UqNPc9FOgjSP" + }, + "outputs": [], + "source": [ + "y + y" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "o0wsi0OfgjSQ" + }, + "outputs": [], + "source": [ + "y / (y + 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F90xKBqCsLrE" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "x8RZiRKigjSQ" + }, + "outputs": [], + "source": [ + "np.sqrt(y**2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3nl1kNlagjSR" + }, + "outputs": [], + "source": [ + "np.max(y), np.min(y), np.sum(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6NT8rRNngjSR" + }, + "source": [ + "## Indexing NumPy Arrays" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RcZ5W6RJgjSS" + }, + "outputs": [], + "source": [ + "np.save('AI.npy', np.arange(16).reshape(4,4))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4qhki4pBgjSS", + "outputId": "fad66d46-65cf-49fa-9df7-011e51dde9d2", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[ 0, 1, 2, 3],\n", + " [ 4, 5, 6, 7],\n", + " [ 8, 9, 10, 11],\n", + " [12, 13, 14, 15]]), (4, 4))" + ] + }, + "execution_count": 35, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "T = np.load('AI.npy')\n", + "T, T.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dfSyCqZKgjST" + }, + "outputs": [], + "source": [ + "T[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Cmu8P9kpgjST" + }, + "outputs": [], + "source": [ + "T[1,0] # more efficient version of T[1][0]# a similar approach can be used for N-d arrays " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RmGhqpahgjSU" + }, + "outputs": [], + "source": [ + "T[0,0:3] # selecting the specific elements for operations " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "r2Z6o3-6gjSU" + }, + "outputs": [], + "source": [ + "(T % 10) == 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "t7cOIhWggjSV" + }, + "outputs": [], + "source": [ + "T[(T % 10) == 0] " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "okbJ-ykCgjSV", + "scrolled": false + }, + "outputs": [], + "source": [ + "np.minimum(T, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "w4ZP5EwSgjSW" + }, + "outputs": [], + "source": [ + "np.max(T)# please try np.maximum and by following the upper example" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ffOVdl9UgjSW" + }, + "source": [ + "## Computation Along Axes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "R5GklAx9gjSX" + }, + "outputs": [], + "source": [ + "A = np.random.random((2,2))\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Fr7bcjqwgjSX", + "scrolled": false + }, + "outputs": [], + "source": [ + "A.sum()# all " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xfjEmGPMgjSX" + }, + "outputs": [], + "source": [ + "np.sum(A)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vb6o-oAfgjSY", + "scrolled": true + }, + "outputs": [], + "source": [ + "A.sum(axis=0) # column wise # col1 # col2 #col3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sjOIT9cYgjSZ" + }, + "outputs": [], + "source": [ + "np.sum(A, axis = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FsZ1_KIBgjSZ" + }, + "outputs": [], + "source": [ + "A.sum(axis=1) # row 1 " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "f3A_VOwQgjSa" + }, + "outputs": [], + "source": [ + "np.sum(A, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Sn8Dpw0SgjSa" + }, + "outputs": [], + "source": [ + "A.max(axis=0), A.argmax(axis=0)# column# the argmax returns the index where max occurs " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_hR3F1MXgjSb" + }, + "outputs": [], + "source": [ + "np.max(A, axis = 0), np.argmax(A, axis = 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DAF_jmjQgjSb" + }, + "source": [ + "## Combining Arrays: Stacking & Concatenation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jAzYfeT2gjSc" + }, + "outputs": [], + "source": [ + "a = np.full(3, 2)\n", + "b = np.full(3, 4)\n", + "c = np.full(3, 6)\n", + "print(a, b, c)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "loVlggYagjSc" + }, + "source": [ + "Stacking creates a new axis. It joins the supplied arrays, which must have the same shape, along that axis.\n", + "Indexing a single element from that axis returns the appropriate input array." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "laxyeFgugjSc" + }, + "outputs": [], + "source": [ + "B = np.stack([a, b, c], axis=1)\n", + "B, B.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8o2HM0OjgjSd" + }, + "outputs": [], + "source": [ + "B = np.hstack((a, b, c))\n", + "B, B.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3EDWsl2RgjSd" + }, + "outputs": [], + "source": [ + "B = np.vstack((a, b, c))\n", + "B, B.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jrafp6NCgjSe" + }, + "outputs": [], + "source": [ + "B[:,1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7OqQyi09gjSf" + }, + "source": [ + "Concatenating arrays joins them along an *existing* aixs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "HmePWZ9igjSf" + }, + "outputs": [], + "source": [ + "C = np.concatenate([B+10, B+50], axis=1 )\n", + "C" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qLPhppfLgjSg" + }, + "source": [ + "## Transposing and Reshaping" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hmed_yqGgjSg" + }, + "outputs": [], + "source": [ + "C, C.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4tRjZ3y6gjSg" + }, + "outputs": [], + "source": [ + "CT = C.T\n", + "CT, CT.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "B-XwXShNgjSh" + }, + "outputs": [], + "source": [ + "CR = C.reshape(9, 2)\n", + "CR, CR.shape # it goes row wise " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "doLOxQhlgjSh" + }, + "source": [ + "## Saving and Loading" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6qwbDzSLgjSi" + }, + "source": [ + "### Saving and loading a single NumPy array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0AVbMISZgjSj", + "scrolled": true + }, + "outputs": [], + "source": [ + "# Save single array\n", + "x = np.random.random((5,))\n", + "print(x)\n", + "\n", + "np.save('tmp.npy', x)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cvrthgEPgjSj" + }, + "outputs": [], + "source": [ + "# Load the array\n", + "y = np.load('tmp.npy')\n", + "\n", + "print(y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jyl1dRtngjSk" + }, + "source": [ + "## Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "k86dDLfKgjSk" + }, + "source": [ + "Need to add parenthesis after the function name:\n", + "\n", + " def myfunction():" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aStB5URUgjSl" + }, + "source": [ + "#### return: \n", + "\n", + "- Exits the function \n", + "\n", + "- Outputs whatever value is places after the return keyword" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "5RV21ynlgjSl", + "outputId": "ef76baab-4663-4a9b-aa9f-d6223c7af49e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Heads'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from random import random\n", + "def flip_coin():\n", + " # generates random number 0-1\n", + " r = random()\n", + " if r > 0.5:\n", + " result = \"Heads\"\n", + " else: \n", + " result = \"Tails\"\n", + " \n", + " return result\n", + "\n", + "flip_coin()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vcUqBnB5gjSm" + }, + "source": [ + "#### Accepting input parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "h7QHMQDogjSm", + "outputId": "619e563e-454d-48c8-f218-54b276489b1c" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 7, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "def double(d):\n", + "\n", + " return 2*d\n", + "\n", + "double(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_DVB7exegjSn" + }, + "source": [ + "**bold text**### Default Parameters\n", + "- Allows you to be more defensive\n", + "- Avoids errors with incorrect parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OVT2lNZhgjSn", + "outputId": "59c9617b-c8c9-49f3-b4b4-8ce52fe2cf0f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "30" + ] + }, + "execution_count": 8, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "def add(a=10, b=20):\n", + " return a+b\n", + "add()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z__9zJIIgjSo" + }, + "source": [ + "### Keyworded Argument" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "maroTD-bgjSo" + }, + "source": [ + "*args: A special operator we can pass to functions. Gathers remaining arguments as a tuple.\n", + "\n", + "This is just a parameter. You can call it whatever you want after the \"*\"!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0CeyxT2SgjSp" + }, + "source": [ + "Instead of doing:\n", + "\n", + " def sum_all_nums(num1, num2, num3, num4):\n", + " return num1+num2+num3+num4" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kuWFXgS6gjSp", + "outputId": "2773de9f-c02f-497e-e4de-48a4958c07ad" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "32\n", + "10\n" + ] + } + ], + "source": [ + "def sum_all_nums(*args):\n", + " total = 0\n", + " for num in args:\n", + " total += num\n", + " return total\n", + "\n", + "print(sum_all_nums(4,6,9,5,8))\n", + "print(sum_all_nums(4,6))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DWlbEaa9gjSq" + }, + "source": [ + "### Lambdas: like a function that has no name (anonymous function)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5bMjy7B6gjSq", + "outputId": "6953c2ef-39df-4a49-80f2-71de724f32eb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49\n" + ] + } + ], + "source": [ + "def square(num): return num*num\n", + "square2 = lambda num: num*num # no return\n", + "print(square2(7))" + ] + } + ], + "metadata": { + "colab": { + "name": "Recitation_1.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/task1-gradient-descent/gradient_descent_soner.ipynb b/task1-gradient-descent/gradient_descent_soner.ipynb new file mode 100644 index 0000000..9edef43 --- /dev/null +++ b/task1-gradient-descent/gradient_descent_soner.ipynb @@ -0,0 +1,1627 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "MmitjbRlPLFS" + }, + "source": [ + "# Gradient Descent\n", + "From previous lecture, we know that, in order to achieve a precise approximation for any given dataset, we define a \"Loss Function\" and try to minimize it. In the case of linear regression we used Mean Squared Error (MSE). Let's see how MSE changes by changing coefficients of linear equation Y=AX+B" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "Y-plSrnTx_34" + }, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "ojhFZDszPLFU" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import linear_model\n", + "from matplotlib.colors import BoundaryNorm\n", + "from matplotlib.ticker import MaxNLocator" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "22Fia7rXPLFW" + }, + "source": [ + "Start with creating a sample dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "executionInfo": { + "elapsed": 948, + "status": "ok", + "timestamp": 1625061999341, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "wXyCIlbvPLFW", + "outputId": "282f238a-e663-48ce-ab73-97329644505f" + }, + "outputs": [ + { + "data": { + "image/png": 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DLcDWHN9LpKI9vKWNoyd76ekPJQTs6shtM7h63rRBbX6jK1CvnnfuoNeMvs5bnSf5Zft7rH22Nav0jxS/jNM1ZvYI4UnWqWbWDvwP4KvAY2Z2G9AG/D6Au+8ys8eA14F+4E53H8jzuYtUhKD69lBc8nMgctsd3u48GcupR4P7rMnjeGnvUWZOGkdLYz17OrpiudPoMpnWji7gTAoI0qd/pHRoMZRIkUteZFRliUE+SG1NFf0DodgHQLz43jPtR08xcfyY2Gunei2tWC1u2v5PpIQFLTJKVl1lDITCZZC9/aHAxwAsXzAjtgI1WuJ4z/od9PSHGFsTfm61hb8dqI97eVCDMpESEL/IaNbkcdSNqeITF0/nExdPZ8LYagZCZ8ogr1vYlLCyFcKjdzMCSySjr/2jOy6npbGeAUcllWVEI3mREhC/yGjzXR9LuC+ov8zU+trYBG3I4ePzzmVyfW1gX/b4176gYQJLLpiiPu5lRDl5kTKUTWMxKX3pcvIK8iIiJU6bhohIINXElz8FeZEKpl2cyp8mXkVGWDHsgapdnCqHRvIiI6wYRs+pNhBJbokgpU8jeZERksnoeaRG+drFqXJoJC8yQjIZPY/kKF+7OFUGjeRFMpCPEXa60fNo5Mi1i1Nl0Eheyk4hygLzNcJONXpWjlwKRSN5KTvxATnXVrn5HmGnGj0rRy6FoiAvZaMQKY/NK5cOavP7e3On8dfDSG8MlfKJjvLVN0bySUFeykZyQM61VW40KM+cPC6yB2q4Be9bcRtzZGOobxjKkUshKMhL2ch3yiMalLe+E/49urVZa0cXzas2ZPwNQQuPZDQpyEtZyUfKI2i7vXjZfkPI5htGMayGlfKiIC9lJR8pj6CgfO7EOvYeOTWsbwjZfMPI56SxCCjIiwwSFJQHQs7Nl6X+hpDrpKpSOlIo6icvFStdYM52043V63fw0NY2blo8e1gj8OTNuuNTOkrbyFC0kbdIgHSpkUzTPvkagatOXgpFQV4qTj5TI/ks21SdvBSCgrxUnHwG5nyOwFUnL4WgIC8VJz4wj62porsvRE2VDTs1ohG4FDMFealI0cB8rKuHDTsPsfWdown3Z1OvrhG4FDMFealIz+3uTMjL7zt2OmEVq+rVpVyohFIqUqqSxY07D9EbsNpV9epSzNKVUKqfvJS1VL3lU02YPq++7lJm8hLkzWyvme0ws+1mti1y7Bwze8bMWiM/J+fjvUSykW6zj6ANPBon1lFjRndfeFJW9epS6vKSrjGzvcAidz8cd+zrwFF3/6qZrQImu/td6V5H6RrJl1RNxjJJu1z5tf9i37HTfGL+uUyurx1ytavIaButFa/Lgasit78HPAekDfIi+TKcWvjkD4YNOw8B4Q8GkVKVr3+9DvzUzF42s9sjx6a5+0GAyM/GPL2XyJCGs0hJ+6xKOcrXSP5ydz9gZo3AM2b2q0yfGPlQuB1g9uzZeTodkewXKal/jJSjvAR5dz8Q+dlhZuuBxcC7Zjbd3Q+a2XSgI8VzHwQehHBOPh/nIwLBi5S0z6pUmpwnXs1sAlDl7icit58B/hpYBhyJm3g9x91XpnstTbxKIXQc7+b277+MGVwwdQI/fHX/sFsCixSjQk+8TgPWm1n09R5296fN7CXgMTO7DWgDfj8P7yWStbWbWtm+7z0AXm0L/9SmHFIpcg7y7v42cEnA8SOER/Mio2KovVqXL5gxrM6TQbQ3qxQr1YZJWeo43s3c6RP56EVTqbLE+6oMzMjrpGq6RVcio0kNyqQsrd3Uyvb297iwoZ5Q0rTTx+edWeSUK+3NKsVOQV7yoljSFclBt7WjCwADrr14Oq+1v0e/e95aAudzAxKRQlCQl7wolta86YJuIT58VFsvxU5BXnJSbOmK0Qi6qq2XYqYgLzkpxnTFSAdd7QwlxUxBXnJSjOkKBV2RM1RCKTmL78t+3cImNuw4OGiTjnxItQGIiKSmIC85e+CWRaxZMZ+5MyYyfkw1753uK0i9uGrRRbKnPV4lL3LZpGM0X1ukHGiPVym4zSuXcvW8abHVpUG92IebblGfd5HhU5CXvGicWMfbnScJOVQbgROw6dItyR8A8b9nO7mr3L3IGQrykrM5qzfSvGpDbHXpgIM7PLy1LeH+dVvacA/X0jev2sCc1Rtjr5H8AZD8e9Cm26kody9yhnLykpF0bQs6jncn1MoDNE8Zz2N/+mEaz6qj43g3K//jNZ57sxMgoZb+yq/9LG2nyKhM8u/K3UulUk5ecpZudNw4sY4f//JALMAD7D1yisX3bWLO6o00TqzjlbZjQLiHTHy6JTnfXltTRdOkcdTWhJP72eTflbsXGUyLoSStodoWREf4S84/h4Pvd3Pw/W56+kNUGXzqkhk8sf0Azas2xJ7vkf9Zt6WNNdddPCjf3jsQYvzYanoHPOvFVcW4MEtktGkkLzFBE5ZDjY6jI/wPNNRz+YVT6R0IB1gn3K/9qS9cQdOkcQnvM3PSOJ764hWx35Pz7e+f7ss4/54sm9y9SCVQTl5iVq/fwUNb2wbtf3rP+h2xSVSAmxbP5gcvt6fMpa/748U8vfNdOk9088Ati/jd+38em5QFaGms55m/+GjhLkSkwhR6j1cpcUOlZA539XD+lAm8ffgkF0ydQGdXT2BjsnMn1rH3yCme3nEo4UPi/dN9XDStni98rIW1/9XKe6f6RuMyRSqSRvIyqDomk+qX2poq/ttvzuThreGyyCDJVS3FsrGISLlRdY2klW7CMtUgwN1j+e91ty2mecr4tKtdQfXrIqNB6RoBUvdgf/6uj3H9t/8f7cdOxx4bXwMfdfmFU/n11raEDwkcbnjgBba3HaN34MyHxWhvLCJSSRTkBUjdg71xYh3HTvYC4a99IWAg5IPSLUEfEtGR+/ULm+gLeVFtLCJSKRTkJUF83jw5Hx+9tf+904OeF/8h8YNt+xKe9/gr+2O3Vb8uMrKUk69A6Rp4xefNU9XIv/jlZWlfP+h508+u4zOXzlT9usgI00i+AiRXtcQH8mipY6oyyqhsRuBBE7nLPtgYey9tyScychTkK0A0qH/4K5uIm/9MmABNV/deN6aKf/vcb/H0rkMZb4o90ptpi0gw1cmXsVRdGaPiJ0Abz6qLrWzNtO5dRIrDqNbJm9k1ZrbbzPaY2apCv5+cEZQbb54yHrPg9MuZuvclga/X0x9K6AEvIsWvoOkaM6sG/gn4XaAdeMnMnnT31wv5vpUsIf8ekBsfCHnKNEp8hcz1C5v44atnqmKqDT55yQyVPYqUmELn5BcDe9z9bQAzexRYDijIF0jypGpQbjw68ZluAvRkbz8tjfW0dnRRZeHdnlT2KFJ6Ch3km4B9cb+3A8G5AMnJUE3GILuqlgduWcSffH8bSy6YoslTkRJW6CBvAccSpvXM7HbgdoDZs2cX+HTK1+aVS7nhgRfYe+QUQEarSodqGJZqFayIlI5CT7y2A7Pifp8JHIh/gLs/6O6L3H1RQ0NDgU+nPM1ZvZHFX9kUC/AA3X0h/vOXB9KmV9QwTKT8FTrIvwS0mNn5ZjYWuBF4ssDvWfaSV6xGq2iiXSDH1hjNU8bz2xcFf2jOWb2R5lUbWLclXC65bksbzas2qHJGpAwVNMi7ez/weeAnwBvAY+6+q5DvWQmSR+DRKhonXBrZN+D85nmTOdU7ENi6QBtei1SOgq94dfengKcK/T6VIN3k6lVzGhKqaH6+u4P2907HqmyGKq1U5YxIeVJbgxIS1HogfsUqDN2DJl1ppXZuEik/6kJZQjIZgSenYpJF8+/P7e5kzYr5zJ0xkTUr5vPALYs0EStShjSSLzFDNf6K/yBIpcpIyL9nUmMvIqVJQb7EZFK7Hv0guGbeuaz+0Y6E0kqA6xY0DRr9r3nqDX6y8yA9/U5tjXHN/OlqYSBSBhTky1D8B8HlF05l75E2qg1CDhc21tPV25/w+DOj//A6tZ5+10SsSJlQTr7MHe7q4ebLzuM//+xKrru0icNdPdyb9A1gzuqNPBQ3OQvhlI3q5kVKn4J8AaTbXm+kz+HeFfNjE6zjx1Tz3um+QROrqpsXKV9K1xRA0PZ6o3kOP3i5Pe3EqurmRcqXgnwepatS2bxy6YjUoAedA4QrasbWVA2qr4/Sdn0i5Unb/+VRx/HulIuV1j7bykNb27hp8eyCju5TnUN1lfHDV/ZjhNuA3ryksOchIiMn3fZ/GsnnUXLao7svxBPbD/DE9jONNwtdg54q9dLZ1UNLYz17Orpoaayns6sn7+8tIsVHE695Fk17rL/jcloa6wFonjJ+RCc148/hpiXn8fDWNn6y611aO7pwoLWji5/selfVMyIVQOmaYRiqx0tyXjyeGQVP2SRLl0bS5KpI6UuXrtFIfhiG6vGSXJJYZdA8ZQLrblvCTUvOo/3Y6ViJZXy5ZXLpZb5KMVU9I1K5lJPPQqY9XpKDau9AiCsunMIVLVO5omUqq9fv4KGtbbEPifgPjPjSy3yWYqp6RqQyKV2ThaC0x1UXNXDoeA8Pfu43E0bGf/L9bTScVZcQVJ/b3Zm2cdhQ1DBMRIKouiZPgtIeb3WeZE9n16DRdlAjsY7j3ax+YifPvP4u7uE0jgEDDtUWLm0MeTiYT62v5XBXNz39ntGm3CIiQZSTz1I07RFyxz1cqZLpPqmNE+t4p/NkLMCHPBzga2uqGPAzAb53IMT4sdX0DvigHHoxtEwQkdJRMUE+H8Gx43g3x0718YVlF/J/7/pYVv1eoptnt3Z0AeGAHrX+jsuZNXkcsyaPi5U9vn+6L6EMMlrXro09RCQbFZOTj0525lK+mPwa96zfwcNb2xhbHR59p3vtXMsYU5VlKk8vIhWdk8/HrkepXqPKyLhiJdcyxnT7u4qIpFL26Zp8tNFN9RovfnnZoH1Sk8WniZJXombTWkC17iIyHGU/ks9HcMzlNeJz6Jls3ZeOat1FJFtlH+QhP8Ex29coxObYuX5IiEjlKduJ16H6yxRaJhOto32OIlIeKrJ3zWiWGkaDd41Z2hSPyiFFpNDKZiQfDazb247ROzD4moLSJIUaSUdLLWdOGsdH5zQmpHjuXT6fJV/ZRND/6yqHFJHhqIgSyuio+PqFTfSFPKNSw1QNwIYb/JPz8PuOnWbdi7/mB9v2xYL36vU7wKD5nPEcOt6tckgRKaiSD/LJgfXxV/bHbqdKkww1KTrc7o/patmT33PvkVOx2yqHFJFCySknb2Z/ZWb7zWx75M+1cffdbWZ7zGy3mV2d+6kGC6phn352HZ+5dGZgPXrH8W7mTp/IzMnjYseide8ONK/awLotbRn3o4mXrtRyqB7z2o5PRAohHyP5v3P3b8QfMLO5wI3APGAG8KyZXeTuA3l4vwRBgXXZBxtjI/DkUsPL/mZTQt8YgO6+ED/+5QFeuHtZzqtKU5VaDtVjXkSkEAqVrlkOPOruPcA7ZrYHWAy8UIg3y6SGPd2WfM1TJtA8ZdyQi54yydWnq2XXYiYRGWn5CPKfN7PPAduAv3T3Y0AT8GLcY9ojxwoik0VCyflyONPu94oLp8RG/ukCca47NWkxk4iMtCGDvJk9C5wbcNc9wLeBewnvd3Ev8E3gjwjvhZEssFbTzG4HbgeYPXt2Ric9HPGj9Ghwv2b+dM6ZMDYhkAcF4uGsXtVCJxEpBkMGeXf/nUxeyMz+Ffhx5Nd2YFbc3TOBAyle/0HgQQjXyWfyXsMVNErPZES9eeVSbnjghVhFTCa5+nzuzyoiMlw5LYYys+nufjBy+0vAEne/0czmAQ8TzsPPADYBLUNNvBbjHq+pcvnVBm/9zScyfrwWOolIoRSyrcHXzWyHmb0GLAW+BODuu4DHgNeBp4E7C1FZk0qqXaCGsztUtPSxKpKAqq2ponnKBK5MURGTj9bGIiL5klOQd/db3P1id/+Qu386OqqP3Hefu3/A3ee4e2aF5nmSqifMcHrFRHP5Dgmlj9/9oyVpH6++7yJSDEp+xWu8VBOkybJt+5tt6aNKJUWkWJRNgzIY3N632mDA4dqLz6WmumrY+6uKiBSzimhQFi1ZnDlpXKwOPtqM8qkdh2KPUwpFRCpJ2QT5aL794KRxXH9pE0e7etm85zADIaduTBWTx4/lIx+Yym1XnK8UiohUjJIP8kHtffcd20+VEZssHaqfjYhIuSr5IB9tV7DhtYMMhJzqKuOTH5rO+6f7mDl5vCY/RaSilXyQv/LrP0sYyQ+EnCe2H4hUziwGNHIXkcpV8nu8bl65lHPPrqM6slip2mD62XVafCQiQhkE+caJdSz7YCMhwvn3EOHRvIiIlEGQhzOLj75z629RV1NFx4merFa1ioiUq7JZDKXGYCJSqQrZoKwopArwVYZy8yJS0coiyEc7P1ZXJe5Vct2CJq1qFZGKVhZBPtr5cSDkVFt4W6qWxnq6evtH+9REREZVydfJRx3u6uHmyxI7P8Zv5SciUonKZuJVRKRSlf3Eq4iIBFOQFxEpYwryIiJlTEFeRKSMKciLiJQxBXkRkTJWVCWUZtYJ/HqYT58KHM7j6Yy2crqecroWKK/r0bUUr2yu5zx3bwi6o6iCfC7MbFuqOtFSVE7XU07XAuV1PbqW4pWv61G6RkSkjCnIi4iUsXIK8g+O9gnkWTldTzldC5TX9ehailderqdscvIiIjJYOY3kRUQkSckHeTP7WzP7lZm9ZmbrzWxS3H13m9keM9ttZleP4mlmzMyuiZzvHjNbNdrnkw0zm2VmPzOzN8xsl5l9MXL8HDN7xsxaIz8nj/a5ZsPMqs3sVTP7ceT3krweM5tkZv8R+e/lDTP7cKleC4CZfSny72ynmT1iZnWlcj1m9u9m1mFmO+OOpTz3XGJZyQd54Blgvrt/CHgTuBvAzOYCNwLzgGuAfzaz6lE7ywxEzu+fgI8Dc4E/jFxHqegH/tLdfwO4DLgzcv6rgE3u3gJsivxeSr4IvBH3e6lezz8AT7v7B4FLCF9TSV6LmTUBXwAWuft8oJrwf++lcj3fJRyX4gWee66xrOSDvLv/1N2jW0C9CMyM3F4OPOruPe7+DrAHWDwa55iFxcAed3/b3XuBRwlfR0lw94Pu/krk9gnCQaSJ8DV8L/Kw7wErRuUEh8HMZgKfAP4t7nDJXY+ZTQR+G/gOgLv3uvt7lOC1xKkBxplZDTAeOECJXI+7/wI4mnQ41bnnFMtKPsgn+SNgY+R2E7Av7r72yLFiVornHMjMmoGFwBZgmrsfhPAHAdA4iqeWrb8HVgLxO8WX4vVcAHQC/yuSevo3M5tAaV4L7r4f+AbQBhwE3nf3n1Ki1xOR6txzigslEeTN7NlI3i35z/K4x9xDOF3wUPRQwEsVeylRKZ7zIGZWDzwO/Lm7Hx/t8xkuM/sk0OHuL4/2ueRBDXAp8G13XwicpHhTGUOK5KuXA+cDM4AJZnbz6J5VweQUF0pij1d3/51095vZrcAngWV+pia0HZgV97CZhL/OFbNSPOcEZjaGcIB/yN1/GDn8rplNd/eDZjYd6Bi9M8zK5cCnzexaoA6YaGbrKM3raQfa3X1L5Pf/IBzkS/FaAH4HeMfdOwHM7IfARyjd64HU555TXCiJkXw6ZnYNcBfwaXc/FXfXk8CNZlZrZucDLcDW0TjHLLwEtJjZ+WY2lvBky5OjfE4ZMzMjnPN9w93vj7vrSeDWyO1bgSdG+tyGw93vdveZ7t5M+O/iv9z9Zkrwetz9ELDPzOZEDi0DXqcEryWiDbjMzMZH/t0tIzwHVKrXA6nPPbdY5u4l/YfwJMQ+YHvkz7/E3XcP8BawG/j4aJ9rhtdzLeEqobeAe0b7fLI89ysIf418Le7v41pgCuFqgdbIz3NG+1yHcW1XAT+O3C7J6wEWANsifz8/AiaX6rVErud/Ar8CdgLfB2pL5XqARwjPJfQRHqnflu7cc4llWvEqIlLGSj5dIyIiqSnIi4iUMQV5EZEypiAvIlLGFORFRMqYgryISBlTkBcRKWMK8iIiZez/A2ZISol2Iyk8AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "A = 2\n", + "B = -20\n", + "x_range = [-20, 100]\n", + "\n", + "def load_dataset(n=150):\n", + " x = (x_range[1] - x_range[0]) * np.random.rand(n) + x_range[0]\n", + " eps = np.random.normal(0,10,n)\n", + " y = A * x + B + eps\n", + " return np.array(y).reshape(-1,1), np.array(x).reshape(-1,1)\n", + "\n", + "y, x = load_dataset()\n", + "\n", + "plt.plot(x,y,'*')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZzEfb7CEPLFX" + }, + "source": [ + "Now, lets define our loss function." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "RhTVY4cWPLFY" + }, + "outputs": [], + "source": [ + "def loss(y_train,y_pred):\n", + " return np.average((y_train-y_pred)**2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NKzXULcgPLFY" + }, + "source": [ + "Our aim is minimizing the defined loss function, let's examine how it varies by changing coefficients of equation. First, keep B constant, and observe how A affects loss." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "executionInfo": { + "elapsed": 13, + "status": "ok", + "timestamp": 1625061999343, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "cg_w97NqPLFY", + "outputId": "ffcc212c-eb20-4361-fda8-d6035fc47fe9" + }, + "outputs": [ + { + "data": { + "image/png": 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0qEQpX7V1bwkLM/Zxy+iutA7znYmrGqKFxEdcPjCebu1b8bQelSjls55dkkmrkEBuGZ1sO0qz0kLiIwIDhDsvSGHL3hIWZuhRiVK+Zvu+Ej5Zv4ebRibTJiLEdpxmpYXEh1w+IJ6u7Vvx9GK9gkspX/P04u1EBAf65MRVDdFC4kOCAgP45dgUNucXs2jzPttxlFIu2rK3mE835HPz6GRiWrWsoxHQQuJzJg6KJ7ldBE9/sR1/m91SKV/19BfbaRUSxG1nt7yjEdBC4nOCAgP45fk9yMgvZlGGHpUo5e0y9hTz2ca9/HR0y+sbOU4LiQ+aNCiepHYRPL1Yj0qU8nZPfbGN1mFB3DqmZR6NgBYSnxQUGMAdY1PYtKeYxZv3246jlDqFjbuLWJixj1vHdCU6ouXcN3IiLSQ+avLgBLrERPDU4m16VKKUl3rqi21EhQXx0zFdbUdxKy0kPio4MIC7LujBxt3FLNAxuJTyOutyC/li835uO7sbUS3oLvb6aCHxYZMHJ5ASF8k/Fm6lWu8rUcqrPPXFNtpEBHNzC7uLvT5aSHxYYIDwvxf1ZEfBUT5as9t2HKWUY3XOYZZsLeC2s7u1qDG1TkULiY8bn9qR/gnRPLloG+VVOre7Ut7gyUXbiGkVwrRRybajeIQWEh8nIvx6fC92Fx7j3R9ybcdRyu99v+MA324/wM/P7UZkaJDtOB5hpZCISBsReV9EtojIZhEZKSIxIrJIRLY7P9vWWf9BEckUka0iMr5O+1AR2eAsmyEiYuPz2HZ2j/YM7xrDjMWZlFZU2Y6jlN8yxvD4gq10ig5j6shk23E8xtYRydPAAmNMb2AgsBl4AFhsjOkBLHZeIyJ9gSlAKjABmCkix6cVew6YDvRwHhM8+SG8xfGjkgNHynn9+1224yjltz7ftJd1uYXcc2GPFjP7oStcKiQi0kpEApznPUXkChE5ox4kEYkCzgFeATDGVBhjCoGJwCxntVnAJOf5RGCOMabcGJMFZALDRKQTEGWMWWZqb6R4o842fictOYbze8fx/Fc7KDpWaTuOUn6nqrqGv32+le6xrbhqSKLtOB7l6hHJN0CYiCRQe7RwC/D6Gb5nN6AAeE1E1ojIyyLSCuhgjMkHcH7GOesnAHVP/uc5bQnO8xPbTyIi00UkXUTSCwoKzjC297vvop4Ul1Xx0jc7bUdRyu+8vyqPnQVH+fX43gQF+lf3s6ufVowxpcCVwL+MMZOBvmf4nkHAEOA5Y8xg4CjOaaxTvXc9beY07Sc3GvOiMSbNGJMWGxvb2Lw+IzU+mssHxvPqd1kUlJTbjqOU3yirrOapL7YzuEsbxqd2sB3H41wuJCIyErgB+NRpO9PLEfKAPGPMCuf1+9QWln3O6Sqcn/vrrN+5zvaJwB6nPbGedr/2qwt7UF5Vw8yvMm1HUcpvzPp+F3uLy/jNhN744zU/rhaSe4AHgY+MMZtEpBuw5Eze0BizF8gVkV5O0wVABjAPmOa0TQPmOs/nAVNEJFREulLbqb7SOf1VIiIjnKu1ptbZxm91i43kmqGJzF6eTc7BUttxlGrxio5VMvOrHZzbM5YR3drZjmOFS4XEGPO1MeYKY8zjTqf7AWPMXU143zuBt0RkPTAIeBR4DBgnItuBcc5rjDGbgPeoLTYLgDuMMcfvvLsdeJnaDvgdwGdNyNRi3HNhT4ICAvjb51tsR1GqxXv+69oLXO6f0KvhlVsol05PicjbwM+BamAVEC0iTxhj/n4mb2qMWQuk1bPoglOs/wjwSD3t6UC/M8nQknWMDuO2s7sy48tMbh1zmMFd2ja8kVKq0fYVl/Had1lMHBRPany07TjWuHpqq68xppjay2v/A3QBbnJXKNV008/tTvvIUB79z2YdZl4pN3l68Xaqqg33jfPfoxFwvZAEO/eNTALmGmMqOcUVUso7RIYGce+4nvyw6zCfb9IpeZVqbpn7S3j3h1yuH96FLu0ibMexytVC8gKwC2gFfCMiSUCxu0Kp5nFtWiIpcZE8vmALldU1tuMo1aI88ulmIkICufuCHrajWOdqZ/sMY0yCMeYSUysbGOvmbKqJggIDeOiS3mQdOMrbK3Jsx1GqxfhmWwFLthZw5/kptIsMtR3HOleHSIkWkSeO3x0uIv+k9uhEebmxveIY1b0dT32xjeIyHTpFqaaqqq7hL59m0CUmwm+GiW+Iq6e2XgVKgGudRzHwmrtCqeYjIjx0SR8Kj1Uyc8kO23GU8nnvpueybd8RHry4N6FB/jMw4+m4Wki6G2P+YIzZ6TwepnbMLOUD+iVEM3lQAq9+l8XuwmO24yjls4rLKnli4TaGJccwoV9H23G8hquF5JiIjDn+QkRGA/qN5EPuG98LAf7x+VbbUZTyWTOX7ODg0Qp+e1kfvxwK5VRcLSQ/B54VkV0isgt4BviZ21KpZpfQJpxbx3TlozW7WZNz2HYcpXxO7qFSXl2axZVDEhiQ2MZ2HK/i6lVb64wxA4EBwABn1N7z3ZpMNbtfjE0htnUof/wkg5oavQ1IqcZ47LMtBATA/eN7247idRo1aL4xpti5wx3gXjfkUW4UGRrEgxf3Zl1uIe+vzmt4A6UUAD/sOsSnG/L52Tnd6RgdZjuO12nK7Ct6gtAHTR6cwJAubfjbgi16ObBSLqipMfxlfgYdokL52bl6jVF9mlJI9NyIDxIRHr6iHwePVjDji+224yjl9d5fnce6vCLuH9+biJAznYapZTttIRGREhEprudRAsR7KKNqZv0To5lyVmde/34XmftLbMdRymsVlVby2GdbGJrUlsmD653JW9FAITHGtDbGRNXzaG2M0dLsw/73ol5EhATyx3kZOjqwUqfwj4VbKSyt4E8TUwkI0LP5p+JfM9Sr/2oXGcq943qyNPMACzN0dGClTrRxdxFvrchm6shkv55rxBVaSPzYjSOS6NWhNX+en0FZZXXDGyjlJ2pqDL+bu5GYViH8alxP23G8nhYSPxYUGMAfruhL3uFjvPjNTttxlPIa76/KY01OIQ9c3Ifo8GDbcbyeFhI/N6p7ey7t34mZX2WSd7jUdhylrCsqreSxBVtIS2rLldrB7hItJIqHLu1DgAi/n7tJO96V3/u/DvZ+2sHuIi0kioQ24dw7ridfbtnPgo17bcdRypqNu4uY7XSw942Psh3HZ2ghUQDcPCqZ1Pgo/jBvk97xrvzS8Q72dtrB3mhaSBRQ2/H+1yv7c+BIuQ41r/zSu+m52sF+hrSQqP8akNiGqSOTeXN5tg41r/zK/uIyHv3PZkZ0i+GqIdrB3lhaSNSP3HdRTzq0DuPBDzdQWV1jO45SHvHw/AzKq2p4dHJ/nbDqDGghUT/SOiyYP16Rypa9Jbz2XZbtOEq53eLN+/h0fT53nZ9Ct9hI23F8khYSdZLxqR24sE8Hnly0ndxDem+JarmOllfxu4830rNDJNPP6W47js+yVkhEJFBE1ojIfOd1jIgsEpHtzs+2ddZ9UEQyRWSriIyv0z5URDY4y2aIHpM2CxHh4YmpiMDv527Ue0tUi/XPhdvILy7jr1cOICRI/64+Uzb33N3A5jqvHwAWG2N6AIud14hIX2AKkApMAGaKSKCzzXPAdKCH85jgmegt3/F7S5ZsLWD++nzbcZRqdmtzC3n9+yxuHJ7E0KS2DW+gTslKIRGRROBS4OU6zROBWc7zWcCkOu1zjDHlxpgsIBMYJiKdgChjzDJT+yfzG3W2Uc3g5lHJDEyM5g/zNnHwSLntOEo1m/Kqan7973XEtQ7j1xN62Y7j82wdkTwF3A/UvSyogzEmH8D5Gee0JwC5ddbLc9oSnOcntp9ERKaLSLqIpBcUFDTLB/AHQYEB/O3qgZSUVfL7eZtsx1Gq2fxrcSbb9x/hr1f1JypM7xlpKo8XEhG5DNhvjFnl6ib1tJnTtJ/caMyLxpg0Y0xabGysi2+rAHp1bM3dF/Tg0/X5LNiop7iU79uQV8RzX+/g6qGJjO0V1/AGqkE2jkhGA1eIyC5gDnC+iMwG9jmnq3B+7nfWzwM619k+EdjjtCfW066a2c/O7U5qfBS//Xgjh49W2I6j1BmrqKrh1++vo12rEH53aV/bcVoMjxcSY8yDxphEY0wytZ3oXxpjbgTmAdOc1aYBc53n84ApIhIqIl2p7VRf6Zz+KhGREc7VWlPrbKOaUXBgAH+/eiCFpZU8/Ime4lK+69klmWzZW8Kjk/sTHaGntJqLN13v9hgwTkS2A+Oc1xhjNgHvARnAAuAOY8zx6fxup7bDPhPYAXzm6dD+om98FHeMTeHjtXtYpFPzKh+0aU8Rzy7JZPLgBC7s28F2nBZF/O0egbS0NJOenm47hk+qqKph4rPfUVBSxuf3nEO7yFDbkZRySVllNROf+Y5DpRUs+tU5tIkIsR3J54jIKmNMWn3LvOmIRHm5kKAAnvrJIIqPVfHghxv0RkXlM55YtI2t+0r429UDtIi4gRYS1Si9Orbmf8f3ZGHGPt5fldfwBkpZtnznQV76dic3DO+iV2m5iRYS1Wi3junG8K4xPPxJho7FpbxaSVkl9723jqSYCP7fpX1sx2mxtJCoRgsMEP557UAA7vv3Oqpr9BSX8k4Pf5JBftExnvjJICJCgmzHabG0kKgzktg2gj9c3peVWYd46dudtuModZIFG/N5f1Ued4xNYUgXHUvLnbSQqDN29dBELu7XkX98vpV1uYW24yj1X3sKj/GbDzYwIDGauy7oYTtOi6eFRJ0xEeGxKwcQ1zqUu+asoaSs0nYkpaiuMdwzZy1V1TXMmDKY4ED9mnM33cOqSaIjgnn6usHkHirl93P1rndl37++3M7KXYf4y+R+JLdvZTuOX9BCoprsrOQY7r6gJx+t2c2Hq/WSYGXPyqxDzFi8nSuHJDB5cGLDG6hmoYVENYtfnp/CsK4x/PbjjWQdOGo7jvJDhaUV3DNnDV1iIvjTxH624/gVLSSqWQQGCE/9ZBDBgQHc8dZqyiqrG95IqWZijOHX76+n4Eg5/7puCJGheqmvJ2khUc0mvk04T/5kIBn5xTpKsPKoF7/ZyaKMfTx0SR/6J0bbjuN3tJCoZnV+7w784rzuvLMyV4dQUR6xYudB/vb5Vi4d0ImbRyXbjuOXtJCoZnfvuJ6M6BbDbz/ewJa9xbbjqBZsf0kZv3xnDUntInj8qgHUTk2kPE0LiWp2QYEBzLhuMK3DgvnF7NV6f4lyi6rqGu56p/b+peduGKr9IhZpIVFuEdc6jGeuG0z2oVLuf3+9Djmvmt0/Fm5j+c5DPDq5P706trYdx69pIVFuM7xbOx6Y0JvPNu5l5lc7bMdRLcgn6/bw/Nc7uH54F64coveL2KaFRLnV/5zdlUmD4vnHwq18uUWn6FVNt2lPEb9+fx1nJbflj5en2o6j0EKi3ExEeOyqAaTGR3H3O2vJ3H/EdiTlww4eKWf6G6toGxHCzBuGEhKkX2HeQP8rKLcLCw7khZvSCAkKYPqb6RRr57s6A5XVNfzirdUcOFLOCzcNJbZ1qO1IyqGFRHlEQptwZt4whJyDpdz9zhqqqmtsR1I+5s/zM1iRdYjHrurPgMQ2tuOoOrSQKI8Z3q0dD09MZcnWAv7y6WbbcZQPee27LN5Yls1tZ3fVwRi9kF54rTzqhuFJZBUc5eWlWSS3i+Dm0V1tR1JebvHmffx5fgYX9e3AAxfrvOveSAuJ8rgHL+lD9qFS/jQ/gy7tIji/dwfbkZSX2ri7iDvfWUNqfDRPTRlEYIDeue6N9NSW8rjAAOHpKYPoGx/FnW+vIWOPDqOiTpZfdIxbZ/1Am/BgXpmWRkSI/t3rrbSQKCsiQoJ4ZdpZRIUHc8vrK8k7XGo7kvIixWWV/PT1dI6WV/PKzWcRFxVmO5I6DS0kypoOUWG8dstZlFZUM/XVlRw6WmE7kvICZZXV3DYrne37Snj2hiH06RRlO5JqgMcLiYh0FpElIrJZRDaJyN1Oe4yILBKR7c7PtnW2eVBEMkVkq4iMr9M+VEQ2OMtmiA796XN6d4zilWlnsfvwMW55/QdKK6psR1IWVdcY7pmzlhVZh/jntQM5t2es7UjKBTaOSKqA+4wxfYARwB0i0hd4AFhsjOkBLHZe4yybAqQCE4CZIhLo/K7ngOlAD+cxwZMfRDWPYV1j+Nd1g9mQV8jts1dTqfeY+CVjDL/9eCMLNu3ld5f1ZeKgBNuRlIs8XkiMMfnGmNXO8xJgM5AATARmOavNAiY5zycCc4wx5caYLCATGCYinYAoY8wyUzu07Bt1tlE+5qLUjjw6uT9fbyvg1/9eR02Njhbsb55ctI13VuZw+3nduXWMXhbuS6xeBiEiycBgYAXQwRiTD7XFRkTinNUSgOV1Nstz2iqd5ye21/c+06k9cqFLly7N+AlUc5oyrAsHj1bw98+3Eh4SyKOT++tERX7iua92MOPLTK5NS+T+8b1sx1GNZK2QiEgk8AFwjzGm+DRfGPUtMKdpP7nRmBeBFwHS0tL0T10vdsfYFI5VVPPMkkxCAgP44xWpWkxauFeWZvH4gi1cPjCev16psxz6IiuFRESCqS0ibxljPnSa94lIJ+dopBOw32nPAzrX2TwR2OO0J9bTrnzcfRf1pLyqmpe+zSIkKICHLumjXy4t1JvLs/nz/AwmpHbkiWsH6g2HPsrGVVsCvAJsNsY8UWfRPGCa83waMLdO+xQRCRWRrtR2qq90ToOViMgI53dOrbON8mEiwkOX9GHqyCRe+jaLfy7cpjMstkDv/ZDL7z7eyIV94phx3WCCA/VuBF9l44hkNHATsEFE1jptDwGPAe+JyK1ADnANgDFmk4i8B2RQe8XXHcaYame724HXgXDgM+ehWgAR4Y+Xp1JRVcMzSzKpqjH8ZkIvPTJpId5ZmcNDH23gnJ6xPHvDEJ1XxMd5vJAYY5ZSf/8GwAWn2OYR4JF62tOBfs2XTnmTgADh0cn9CQwQnv96B2WV1fzh8r5aTHzcq0uz+NP8DMb2iuW5G4cSGhTY8EbKq+ngNcqrBQQIf5nUj9CgQF79LovyqmoemdSfAD2X7pNmfpXJ3xZsZUJqR2ZcN1iPRFoILSTK64kIv7usD2HBAcz8agfllTX87eoBBOk5dZ9hjOHJRduY8WUmEwfF889rBup/vxZEC4nyCSLC/RN6Ex4cyD8XbaPwWCXPXD9YR4T1AdU1ht/P3chbK3L4SVpnHr2yv16d1cLonwTKp9x5QQ/+MqkfX23dz3UvreDgkXLbkdRpHKuo5uezV/HWito71h+7SotIS6SFRPmcG0ck8dyNQ9mSX8zVzy8j95AOQe+NDh+t4IaXl/PF5n08fEUqv5nQWy+UaKG0kCifND61I2/9z3AOHa1g8szvWZNz2HYkVUf2waNc9fz3bNxTzMzrhzBtVLLtSMqNtJAon5WWHMMHt48kPCSAn7y4nI/X7LYdSQHfZx5g4rPfcehoBW/+dBgX9+9kO5JyMy0kyqelxLVm7h1jGNy5Dfe8u5bHF2zRkYMtenPZLm56dSWxkaHMvWM0w7u1sx1JeYAWEuXzYlqF8Oatw7luWBee+2oH099Mp7is0nYsv1JRVcNvP97A7+Zu4ryesXz4i1EktWtlO5byEC0kqkUICQrg0cn9ePiKVJZsLeCKfy1l054i27H8wu7CY1z7wjJmL8/h5+d258WpabQOC7YdS3mQFhLVYogI00YlM2f6CI5VVjN55ve8szJHB3x0o8Wb93HJ09+yY/8RZt4whAcu7q2X9/ohLSSqxTkrOYZP7zqb4V1jePDDDdz73jqOlutc8M2psrqGv/5nM7fOSiehTTif3DmGS7RT3W/pbcGqRWofGcrrtwzj2SWZPPnFNlZlH+af1w7krOQY29F8Xub+En717jo27C7ihuFd+N1lfQkL1oEX/ZkekagWKzBAuOuCHrz3s5EYDNe+sIzHPttCeVV1wxurk9TUGF7+dieXzFhK3uFSZt4whEcm99ciovSIRLV8ZyXH8Nnd5/DIpxk8//UOvtq6n79fPZD+idG2o/mMnIOl3P/BOpbvPMSFfeJ49Mr+xLUOsx1LeQnxt47ItLQ0k56ebjuGsuTLLfv4zQcbOHiknKkjk7nvop56hdFpVFTV8NK3O5mxeDtBAcIfLk/lmrREHerED4nIKmNMWn3L9IhE+ZXze3fgi3tj+MfnW5m1bBefbcznj5enMqFfR/1yPMHynQf57ccbydx/hIv7deT3l/elU3S47VjKC+kRifJba3IO8/8+2khGfjFjUtrz4CW9SY3X0125h0r5++dbmbduD4ltw/nzxH6M7R1nO5ay7HRHJFpIlF+rqq7hjWXZzPhyO0XHKrlycCL/O76nX/7lXVhawTNfZvLGsmwCAuC2s7vxi/NSCA/RznSlheRHtJCo+hQdq2Tmkkxe+34XAkwdmcRt53Tziw7l4rJK3lyWzQtf76CkvIprhiZy77hedIxu+Z9duU4LSR1aSNTp5B0u5YlF2/h4zW6CAgOYclZnfnZudxLatLwjlENHK3h1aRazlu2ipKyK83vHcf+EXvTuGGU7mvJCWkjq0EKiXLHrwFGe/3oHH6zOwxi4fGA8N41MYnDnNj7fKZ+5v4TZy3N494dcyqqqmZDakTvGptAvQfuH1KlpIalDC4lqjD2Fx3jp2538Oz2PI+VVpMZHcdOIJK4YFO9T88VXVNWwMGMvs5dns3znIUICA7hsYCd+cV53UuJa246nfIAWkjq0kKgzcaS8irlrd/Pmsmy27C0hIiSQC/t04PKB8ZzTsz2hQd7XIV1dY1iRdZBP1uWzYGM+h0sr6RwTzvXDkrg2LZF2kaG2IyofooWkDi0kqimMMazKPsyHa3bz2YbaL+eosCAu7NuBc3vGMialvdUv6JKySpbvPMQ32wpYsGkvBSXl/y16k4ckcG6PWAJ0dF51BrSQ1KGFRDWXyuoalmYe4JO1e/hy634KSysRgX7x0YxOac+gztEMSGxDp+gwt/WrHDhSzoa8ItbmFrJsx0FW5xymqsYQERLIOT1iuXxgPOf3jtNLeFWTaSGpQwuJcofqGsOG3UV8u62Ab7YXsCankCpnyt/2kaH0S4giuV0rkttFkNS+FZ3bhtM2IoTo8GCCAk89dmp1jaGkrJLDpZXkHS4l+2ApOYdK2XXgKJv2FLO78BjAfwvY2T3ac3aPWIYktfHK023Kd7XoQiIiE4CngUDgZWPMY6dbXwuJ8oSyymo25xezPq+IdXmFbM4vIfvgUUorTh55uHVoEK3Dgn50ysmY2vs7jpRXceI/0ZCgADq3DadPpygGJrahf2I0/RKiiQz1nc5/5Xta7FhbIhIIPAuMA/KAH0RknjEmw24y5e/CggMZ3KUtg7u0/W+bMYYDRyrIPniU3YXHKCytrH0cq6D4WBWG/6sYgtA6LIio8GCiw4NpEx5MQttwktpF0KF1mPZzKK/i04UEGAZkGmN2AojIHGAioIVEeR0RIbZ1KLGtQ6n3zzqlfJSvT2yVAOTWeZ3ntP2IiEwXkXQRSS8oKPBYOKWU8ge+XkjqO74/qdPHGPOiMSbNGJMWGxvrgVhKKeU/fL2Q5AGd67xOBPZYyqKUUn7J1wvJD0APEekqIiHAFGCe5UxKKeVXfLqz3RhTJSK/BD6n9vLfV40xmyzHUkopv+LThQTAGPMf4D+2cyillL/y9VNbSimlLNNCopRSqkl8foiUxhKRAiD7DDdvDxxoxjjNRXM1juZqPG/Nprkapym5kowx9d4/4XeFpClEJP1UY83YpLkaR3M1nrdm01yN465cempLKaVUk2ghUUop1SRaSBrnRdsBTkFzNY7majxvzaa5GsctubSPRCmlVJPoEYlSSqkm0UKilFKqSbSQ1ENEJojIVhHJFJEH6lkuIjLDWb5eRIZ4Sa7zRKRIRNY6j997KNerIrJfRDaeYrmt/dVQLo/vLxHpLCJLRGSziGwSkbvrWcfj+8vFXDb2V5iIrBSRdU6uh+tZx8b+ciWXlX+PznsHisgaEZlfz7Lm31/GGH3UeVA7+OMOoBsQAqwD+p6wziXAZ9TOhzICWOEluc4D5lvYZ+cAQ4CNp1ju8f3lYi6P7y+gEzDEed4a2OYl/3+5ksvG/hIg0nkeDKwARnjB/nIll5V/j8573wu8Xd/7u2N/6RHJyf47fa8xpgI4Pn1vXROBN0yt5UAbEenkBbmsMMZ8Axw6zSo29pcruTzOGJNvjFntPC8BNnPyrJ4e318u5vI4Zx8ccV4GO48TrxCysb9cyWWFiCQClwIvn2KVZt9fWkhO5sr0vS5N8WshF8BI53D7MxFJdXMmV9nYX66ytr9EJBkYTO1fs3VZ3V+nyQUW9pdzmmYtsB9YZIzxiv3lQi6w8//XU8D9QM0pljf7/tJCcjJXpu91aYrfZubKe66mdjycgcC/gI/dnMlVNvaXK6ztLxGJBD4A7jHGFJ+4uJ5NPLK/GshlZX8ZY6qNMYOonQF1mIj0O2EVK/vLhVwe318ichmw3xiz6nSr1dPWpP2lheRkrkzfa2OK3wbf0xhTfPxw29TO0xIsIu3dnMsVXjklsq39JSLB1H5Zv2WM+bCeVazsr4Zy2f7/yxhTCHwFTDhhkdX/v06Vy9L+Gg1cISK7qD39fb6IzD5hnWbfX1pITubK9L3zgKnO1Q8jgCJjTL7tXCLSUUTEeT6M2v++B92cyxU29leDbOwv5/1eATYbY544xWoe31+u5LK0v2JFpI3zPBy4ENhywmo29leDuWzsL2PMg8aYRGNMMrXfEV8aY248YbVm318+P0NiczOnmL5XRH7uLH+e2hkZLwEygVLgFi/JdTVwu4hUAceAKca5TMOdROQdaq9QaS8iecAfqO18tLa/XMxlY3+NBm4CNjjn1wEeArrUyWVjf7mSy8b+6gTMEpFAar+I3zPGzLf979HFXFb+PdbH3ftLh0hRSinVJHpqSymlVJNoIVFKKdUkWkiUUko1iRYSpZRSTaKFRCmlVJNoIVHKC4jIZBExItLbdhalGksLiVLe4TpgKbU3kSnlU/Q+EqUsc8a32gqMBeYZY/SoRPkUPSJRyr5JwAJjzDbgULNMNKSUB2khUcq+66gdYA/n53UWsyjVaHpqSymLRKQdtaOx7qd2KO9A52eSrXGZlGosPSJRyq6rqZ2tLskYk2yM6QxkAWMs51LKZVpIlLLrOuCjE9o+AK63kEWpM6KntpRSSjWJHpEopZRqEi0kSimlmkQLiVJKqSbRQqKUUqpJtJAopZRqEi0kSimlmkQLiVJKqSb5/8sUvxwHHqW5AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "b=-20\n", + "loss_values = np.zeros(y.shape)\n", + "a_values = np.zeros(y.shape)\n", + "for i,a in enumerate(np.linspace(0,4,y.shape[0])):\n", + " a_values[i][0] = a\n", + " y_pred= a*x+b\n", + " loss_values[i][0] = loss(y_pred,y)\n", + "\n", + "plt.xlabel('A')\n", + "plt.ylabel('Loss')\n", + "plt.plot(a_values,loss_values)\n", + "plt.show()\n", + "\n", + " \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2esG52gVPLFZ" + }, + "source": [ + "Now, we can do the same thing while keeping A constant." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "executionInfo": { + "elapsed": 11, + "status": "ok", + "timestamp": 1625061999343, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "396t_tIMPLFa", + "outputId": "8b8fafac-c540-4797-f3f8-8c7dda0b6ba0" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "a=2\n", + "loss_values = np.zeros(y.shape)\n", + "b_values = np.zeros(y.shape)\n", + "for i,b in enumerate(np.linspace(-50,50,y.shape[0])):\n", + " b_values[i][0] = b\n", + " y_pred= a*x+b\n", + " loss_values[i][0] = loss(y_pred,y)\n", + "\n", + "plt.xlabel('B')\n", + "plt.ylabel('Loss')\n", + "plt.plot(b_values,loss_values)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vZ5Zd8OYPLFa" + }, + "source": [ + "For both cases, a clear parabolic relationship between coefficients and loss is observed. However, for simplicty we iterate over those values one by one. The real loss function graph for this problem can be imagined as a valley-like surface. As number of coefficients and variables increase, the complexity of loss function increases, too. While we can easily find the 0-derivate points using partial derivatives of defined loss function and coefficients, the problem becomes harder to solve for more complex systems, so we need another method." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E71YnbIqPLFa" + }, + "source": [ + "Let's assume that we have a robot which is blind on a valley and it is trying to find the lowest point it can get. In order to achieve that it can use slope of the surface it is standing, and move where it goes downwards. For visual simplicity, we will assume that B is constant.
From loss function we can derive: $$L=\\frac{1}{N}\\sum \\left( Ax_i+B-y_i \\right)^2$$ \n", + "$$\\frac{\\partial L}{\\partial A}=\\frac{1}{N}\\sum \\left[2x_i\\left( Ax_i+B-y_i \\right)\\right]$$\n", + "$$\\frac{\\partial L}{\\partial A}=\\frac{1}{N}\\left[2A\\sum x_i^2 + 2B\\sum x_i - 2\\sum x_iy_i\\right]$$ " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "executionInfo": { + "elapsed": 297, + "status": "ok", + "timestamp": 1625062104761, + "user": { + "displayName": "bogaziciAI Community", + "photoUrl": "", + "userId": "01551662724788664199" + }, + "user_tz": -180 + }, + "id": "8f-yUi9XPLFb", + "outputId": "5dbe6262-1dac-4dca-c75b-991db6ad2c70" + }, + "outputs": [ + { + "data": { + "image/png": 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7JUr5q+37ilm0NZ87R3ajVaj/DFx1NlpI/MQ1AzvTvX1LXtC9EqX81vSlGbQMDuDOkfG2ozQqLSR+IsAlPHRJAtv2FbNoq+6VKOVv0vOL+XRjLrcPj6d1WLDtOI1KC4kfuWZAZ7q1b8kLS/QMLqX8zQtL0gkLCvDLgavORguJHwkMcPHgmATS8opYnJZvO45Sqo627Svis0153DEynrYtm9beCGgh8TvjB3Umvl0YL3yZTnMb3VIpf/XCl+m0DA7kngua3t4IaCHxO4EBLh68OJGteUUs3qp7JUr5uq25RXy+eR93jWx6fSMnaCHxQ9cN6kxcuzBeWKJ7JUr5uue/3EGr0EDuHtU090ZAC4lfCgxwMXVMAltyi1iStt92HKXUaWzeW8iirfncPaobkWFN57qRk2kh8VMTBsfQtW0Yzy/ZoXslSvmo57/cQURoIHeN6mY7ikdpIfFTQQEuHr4kkc17i1io9+BSyudsyD7Cl2n7ueeC7kQ0oavYa6OFxI9NGBzDFe32UTn/p1RV6SiKSvmS57/cQeuwIO5oYlex10YLiR8LcAn39a3imoovSP3sVdtxlFKOtXsOs3R7Afdc0L1J3VPrdLSQ+LkB4+4mM6AbMeueo6ys1HYcpRTw98U7aNsymMkj4m1H8QotJH5OXAEcG/UrYs0+1n0yzXYcpZq9ZTsP8G36Ae67sDvhIYG243iFlUIiIq1FZJ6IbBORNBEZLiJtRWSxiKQ7z21qzP+EiGSIyHYRubxG+xAR2eRMmyYiYuP72Nb3whtJC+pLwtbplBwrsh1HqWbLGMOzC7fTKTKUScPjbcfxGlt7JC8AC40xvYCBQBrwOLDEGJMILHHeIyJ9gIlAX2AcMENETgwr9hIwBUh0HuO8+SV8hbhcyKVP0p4jbJj3rO04SjVbX2zZx4bsI/zs0sQmM/phXdSpkIhISxFxOa/PE5FrReScepBEJAIYDbwGYIwpN8YcAcYDs5zZZgHXOa/HA3ONMWXGmEwgAxgqIp2ACGPMclN9IcVbNZZpdnoNu5wNLYbRN/MNCg8X2I6jVLNTWeXmz19sp0dUS25IirUdx6vqukfyDRAqIjFU7y3cCbx5juvsDhQAb4jIOhF5VURaAh2MMXkAznO0M38MkF1j+RynLcZ5fXL7KURkioikikhqQUHT/ZFteeXvCDclbPvX721HUarZmbcmh10Fx3j08l4EBjSv7ue6flsxxpQA1wP/MMZMAPqc4zoDgSTgJWPMYOAYzmGs0627ljZzhvZTG42ZaYxJNsYkR0VF1Tev30jon8KaiEsYsHcOB/P22I6jVLNRWlHF81+mM7hray7v28F2HK+rcyERkeHArcBnTtu5no6QA+QYY1Y67+dRXVjyncNVOM/7a8zfpcbysUCu0x5bS3uz1mH8UwRSxa4P/9d2FKWajVnLdrOvqJRfjutFczznp66F5GfAE8BHxpgtItIdWHouKzTG7AOyRaSn03QJsBWYD0x22iYDnziv5wMTRSRERLpR3am+yjn8VSwiKc7ZWpNqLNNsdU3oR2q7axm0fz65O7fYjqNUk1d4vIIZ/9nJhedFkdK9ne04VtSpkBhjvjbGXGuMedbpdD9gjHm4Aet9CHhHRDYCg4CngWeAsSKSDox13mOM2QK8T3WxWQhMNcacuB/I/cCrVHfA7wQ+b0CmJqPHDU9RSQC5H//adhSlmrx/fr2TwuMVPDau59lnbqLqdHhKRN4F7gOqgDVApIg8Z4z5y7ms1BizHkiuZdIlp5n/j8Afa2lPBfqdS4amLDomjpWxtzBs75tsW/stvZIusB1JqSYpv6iUN77PZPygzvTtHGk7jjV1PbTVxxhTRPXptf8GugK3eyqUari+N/0vR2hF5cL/wbjdtuMo1SS9sCSdyirDL8Y2370RqHshCXKuG7kO+MQYU8FpzpBSviE8sh0ZvafSr3wDa7+cazuOUk1Oxv5i3ludzS3DutK1XZjtOFbVtZC8DOwGWgLfiEgcoPfi8HGDJjxCtiuGqOV/oKK8zHYcpZqUP36WRlhwAD+9JNF2FOvq2tk+zRgTY4y50lTLAsZ4OJtqoMDgEA6P/DVdzV7WfPh323GUajK+2VHA0u0FPHRxAu3CQ2zHsa6ut0iJFJHnTlwdLiJ/o3rvRPm4/mMmsiV4AD23vUjRkQO24yjl9yqr3Pzhs610bRvWbG4TfzZ1PbT1OlAM3OQ8ioA3PBVKNR5xuQi56k9EmqNsfe9J23GU8nvvpWazI/8oT1zRi5DA5nNjxjOpayHpYYx50hizy3k8RfU9s5QfSBg4ijWtLyMpdy77srbZjqOU3yoqreC5RTsYGt+Wcf062o7jM+paSI6LyKgTb0RkJHDcM5GUJ8Te+CeqcJH7wRO2oyjlt2Ys3cnBY+X8+urezfJWKKdT10JyHzBdRHaLyG7gReBej6VSja5Tlx6si72NpKKv2J66xHYcpfxO9qESXv8uk+uTYhgQ29p2HJ9S17O2NhhjBgIDgAHOXXsv9mgy1egGTHySA7TGLPwf3FV6kaJS9fHM59twueCxy3vZjuJz6nXTfGNMkXOFO8AjHsijPCi8VWuyBj5Cr8o0Vn36su04SvmN1bsP8dmmPO4d3YOOkaG24/ichoy+ogcI/VDS+AdJDzyPHuufpajwkO04Svk8t9vwhwVb6RARwr0X6jlGtWlIIdFbpPghcQUgV/6FKA6z+V29O7BSZzNvbQ4bcgp57PJehAWf6zBMTdsZC4mIFItIUS2PYqCzlzKqRpaQdBGpba4ked9csravtx1HKZ9VWFLBM59vY0hcGyYMrnUkb8VZCokxppUxJqKWRytjjJZmP9Z94p8plRAKP3xE7w6s1Gn8ddF2jpSU87vxfXG59Gj+6TSvEerVf7Xt0IW0nlMZULaG9V++azuOUj5n895C3lmZxaTh8c16rJG60ELSjA254VF2u7rSYflTlJYctR1HKZ/hdht+88lm2rYM5udjz7Mdx+dpIWnGAoNDOHrJ03Q2+1k/93e24yjlM+atyWHdniM8fkVvIlsE2Y7j87SQNHP9Rl7DmvCLGJT1OnlZ223HUcq6wpIKnlm4jeS4NlyvHex1ooVEEfPjv+HGxb65P9WOd9Xs/V8Hez/tYK8jLSSKjl0S2JT4AIOPL2fdotm24yhlzea9hcx2Otj7dI6wHcdvaCFRAAy56Ql2BXQjZsWTesW7apZOdLC30w72etNCooDqjveqq/5OlDnMltmP2Y6jlNe9l5qtHeznSAuJ+q/EpDGkRk1g6P55bFv7te04SnnN/qJSnv53Gind23JDknaw15cWEvUDvW/7K4clkoDPfk5FRbntOEp5xVMLtlJW6ebpCf11wKpzoIVE/UCr1u3ITvktiVU7Wf3+M7bjKOVxS9Ly+WxjHg9fnED3qHDbcfySFhJ1ikGXTWZTi6EM3PEiuVnptuMo5THHyir5zcebOa9DOFNG97Adx29ZKyQiEiAi60RkgfO+rYgsFpF057lNjXmfEJEMEdkuIpfXaB8iIpucadNE90kbhbhcRE38By4M+XMf1GtLVJP1t0U7yCsq5U/XDyA4UP9ffa5sbrmfAmk13j8OLDHGJAJLnPeISB9gItAXGAfMEJEAZ5mXgClAovMY553oTV/HuF5sTJzK4OMrWPfvV23HUarRrc8+wpvLMrltWBxD4tqcfQF1WlYKiYjEAlcBNX+hxgOznNezgOtqtM81xpQZYzKBDGCoiHQCIowxy40xBnirxjKqEQz58f+wPbAn3VOf4lB+tu04SjWassoqHv3XBqJbhfLouJ624/g9W3skzwOPATWPmXQwxuQBOM/RTnsMUPNXLMdpi3Fen9x+ChGZIiKpIpJaUFDQKF+gOQgMCiL4+pcIM6Xsfnuq7ThKNZp/LMkgff9R/nRDfyJC9ZqRhvJ6IRGRq4H9xpg1dV2kljZzhvZTG42ZaYxJNsYkR0VF1XG1CqBbnyGs6XYvSUe/Zt3CN2zHUarBNuUU8tLXO7lxSCxjekaffQF1Vjb2SEYC14rIbmAucLGIzAbyncNVOM/7nflzgC41lo8Fcp322FraVSNLvvW3pAck0HXFkxwpyLMdR6lzVl7p5tF5G2jXMpjfXNXHdpwmw+uFxBjzhDEm1hgTT3Un+lfGmNuA+cBkZ7bJwCfO6/nARBEJEZFuVHeqr3IOfxWLSIpzttakGsuoRhQUFIxrwgxamaNkvP2g7ThKnbPpSzPYtq+Ypyf0JzJMD2k1Fl863+0ZYKyIpANjnfcYY7YA7wNbgYXAVGNMlbPM/VR32GcAO4HPvR26uejRbxhrut5NctGXrF/8ju04StXbltxCpi/NYMLgGC7t08F2nCZFqk94aj6Sk5NNamqq7Rh+qbyslOw/p9C66hDywHLaRus9iZR/KK2oYvyL33OopJzFPx9N67Bg25H8joisMcYk1zbNl/ZIlI8LDgnFdcMrtDLH2PPmT/RCReU3nlu8g+35xfz5xgFaRDxAC4mql259zmdN4sMMKllG6sf/sB1HqbNasesgr3y7i1uHddWztDxEC4mqt6E3/5rNwQPps+FpcjPTzr6AUpYUl1bwi/c3ENc2jP+5qrftOE2WFhJVbwEBAbS79VXcCEXv3kVVZaXtSErV6qlPt5JXeJznfjyIsOBA23GaLC0k6px0ijuPrYP/l14VW0l990nbcZQ6xcLNecxbk8PUMQkkddV7aXmSFhJ1zoZeex9rwi8kaedL7Fj3re04Sv1X7pHj/PKDTQyIjeThSxJtx2nytJCocyYuF4l3vsIhaU3Y/J9QXHjIdiSlqHIbfjZ3PZVVbqZNHExQgP7MeZpuYdUgEe06cGjcdDq589nx2j3QzK5LUr7nH1+ls2r3If4woR/x7VvajtMsaCFRDdY75QpWxU1hSNGXpH7you04qhlblXmIaUvSuT4phgmDY8++gGoUWkhUoxg66Wk2Bw+gz7rfk5O+3nYc1QwdKSnnZ3PX0bVtGL8b3892nGZFC4lqFAGBgbSf9BalEkz53MmUHj9mO5JqRowxPDpvIwVHy/jHzUmEh+ipvt6khUQ1mo6x3cge/Ve6V+1m/Sv3246jmpGZ3+xi8dZ8fnVlb/rHRtqO0+xoIVGNauDFE1nV6VZSDn3Cqo+n246jmoGVuw7y5y+2c9WATtwxIt52nGZJC4lqdEl3Pc+W4AEMWPckmZuX246jmrD9xaU8OGcdce3CePaGAVQPTaS8TQuJanSBQcFE3/UORdKKkA/uoPhIge1IqgmqrHLz8Jx1FJdW8NKtQ7RfxCItJMojojp2peCKmbR3F7B75m0Yd9XZF1KqHv66aAcrdh3i6Qn96dmxle04zZoWEuUxfYeNZXXPR+lfsoJVb/3KdhzVhHy6IZd/fr2TW4Z15fokvV7ENi0kyqNGTPwlqyPGcn7my6xf+i/bcVQTsCW3kEfnbeD8+Db89pq+tuMotJAoDxOXi/73vUFWYBzd//NTsnZssB1J+bGDR8uY8tYa2oQFM+PWIQQH6k+YL9A/BeVxoWGtaDHpPaokANeciRRp57s6BxVVbh54Zy0Hjpbx8u1DiGoVYjuScmghUV7RMa4XuZe/Qgd3Ptn//BGV5WW2Iyk/8/sFW1mZeYhnbujPgNjWtuOoGrSQKK/pO3wcawb8lr6l61g38169U7Cqsze+z+St5Vncc0E3vRmjD9JCorxq+A0Ps6zjbZx/4CNWzH3GdhzlB5ak5fP7BVu5rE8HHr9Cx133RVpIlNcN+8kLrA8bwfnbnmX90nm24ygftnlvIQ/NWUffzpE8P3EQAS69ct0XaSFRXhcQGMh5D8whKzCexP9MZdfGZbYjKR+UV3icu2etpnWLIF6bnExYsF657qu0kCgrwsJb0+qujyiScCI/vJm83Wm2IykfUlRawV1vpnKsrIrX7jif6IhQ25HUGWghUdZExXTj+I/fJ5AKKmddz+GCXNuRlA8orajinlmppOcXM/3WJHp3irAdSZ2F1wuJiHQRkaUikiYiW0Tkp057WxFZLCLpznObGss8ISIZIrJdRC6v0T5ERDY506aJ3vrT73TvPYS9V7xJlLuAgpfHU3K00HYkZVGV2/CzuetZmXmIv900kAvPi7IdSdWBjT2SSuAXxpjeQAowVUT6AI8DS4wxicAS5z3OtIlAX2AcMENEApzPegmYAiQ6j3He/CKqcfQZdhlpI5+nR0U6O168kQq9xqRZMsbw6483s3DLPn5zdR/GD4qxHUnVkdcLiTEmzxiz1nldDKQBMcB4YJYz2yzgOuf1eGCuMabMGJMJZABDRaQTEGGMWW6MMcBbNZZRfmbwZbextv+vGVS6ik3/mIi7stJ2JOVlf1+8gzmr9nD/RT24e1Q323FUPVjtIxGReGAwsBLoYIzJg+piA0Q7s8UA2TUWy3HaYpzXJ7fXtp4pIpIqIqkFBXp7Dl91/o3/j2XdHiKp+CvWTJ+kt55vRl76z06mfZXBTcmxPHZ5T9txVD1ZKyQiEg58APzMGFN0pllraTNnaD+10ZiZxphkY0xyVJQec/VlIyb/gWWxP+H8w5+xasY9GLfbdiTlYa99l8mzC7dxzcDO/Ol6HeXQH1kpJCISRHUReccY86HTnO8crsJ53u+05wBdaiweC+Q67bG1tCs/N/yuv7Ciw80MO/ABy2c+pMWkCXt7RRa/X7CVcX078txNA/WCQz9l46wtAV4D0owxz9WYNB+Y7LyeDHxSo32iiISISDeqO9VXOYe/ikUkxfnMSTWWUX5MXC6G3TuDVe0nMGLfbFa89ogWkybo/dXZ/ObjzVzaO5ppNw8mKECvRvBXNv7kRgK3AxeLyHrncSXwDDBWRNKBsc57jDFbgPeBrcBCYKox5sTB8/uBV6nugN8JfO7Vb6I8Rlwuku9/jVVtrmb43jdYNXOqFpMmZM6qPfzyw42MPi+K6bcm6bgifk5MM7sDa3JysklNTbUdQ9WRu6qKVTN+QsrBD1kVdQPn3/8K4go4+4LKZ73+XSa/W7CVMT2jeOm2IYQG6Z+nPxCRNcaY5Nqm6X8DlE9zBQQwbOprLO9wM0MLPmD1i5P01GA/NuM/GfzO6RN5+fZkLSJNhBYS5fPE5SLl3hksi7mToYcWsHbaRCorym3HUvVgjOG5Rdv588LtjB/UmRdvGayHs5oQ/ZNUfkFcLkbc8zzL4+4juWgxW567Sm+n4ieq3NVXrE/7KoMfJ3fhuZsGEagd602K/mkqvzL8zmdZ2efX9CtZTc7zl3Jo/17bkdQZHC+v4r7Za3hnZfUV68/c0F9P8W2CtJAovzPspkfZMPJFulZkUvLSJeTu2mo7kqrF4WPl3PrqCr5My+epa/vyy3G99GLDJkoLifJLSZfdRuZVcwg3xYS8NY7ta5bajqRqyDp4jBv+uYzNuUXMuCWJySPibUdSHqSFRPmt3kPHUnjzAsokhPj5PyL105dtR1LAsowDjJ/+PYeOlfP2XUO5on8n25GUh2khUX4trudgWjzwNTtDepG85jGWz3xITw+26O3lu7n99VVEhYfwydSRDOveznYk5QVaSJTfaxPVmYRffMnKtuMZnvsWm/52FUWFh2zHalbKK938+uNN/OaTLVx0XhQfPjCCuHYtbcdSXqKFRDUJwSGhDH3wTVb0+hV9S1ZR+PxIdm5cZjtWs7D3yHFuenk5s1fs4b4LezBzUjKtQoNsx1JepIVENRnicpEy8ZekXzmHEFNK7AfXsmreX/UeXR60JC2fK1/4lp37jzLj1iQev6KXnt7bDGkhUU1O72HjCHjgO7a3GMjQzb9n7fM3cqzosO1YTUpFlZs//TuNu2elEtO6BZ8+NIortVO92dJCopqkdtEx9H10Ecvi7mdQ4Vcc/nsKaSsX2Y7VJGTsL+b6Gct4+Ztd3DqsKx8+MIL49tof0pxpIVFNVkBAACPufIYdV8zBhZvz/n0Ty19+iLLSEtvR/JLbbXj1211cOe07cg6XMOPWJP44ob/eeFFpIVFNX++UK4j4+SrWtLua4XlvkfvnFNLXf2c7ll/Zc7CEW15dwR8+S2N0Ynu++PloPZSl/ksLiWoWwiPaMPTh2WwYPZNwdxHdP7qaFdPvoVhPEz6j8ko305dmMPbvX7Mpp5A/3zCAVyYlE90q1HY05UO0kKhmZeDFPyb4p6mkRk1g6P5/cfzvQ1j3+Rt6ZlctVuw6yJXTvuUvX2zn4l7RfPmLC7np/C56vyx1Ch0hUTVb21O/IvDzX9CjahebQgYTdtXT9BgwwnYs67IPlfCXL7Yzf0MusW1a8Pvx/RjTK9p2LGXZmUZI1EKimrXKinJS//UXeu2YQYQ5xprWl9H1R0/TITbBdjSvO1JSzotfZfDW8ixcLrjngu48cFECLYK1M11pIfkBLSSqNoWHC0h770kG572PAdZ3vJGECb+ifceutqN5XFFpBW8vz+Llr3dSXFbJj4bE8sjYnnSM1H4Q9X+0kNSghUSdSV7WdnI+/A1JRxZRSSDro64h7tpf0bFrou1oje7QsXJe/y6TWct3U1xaycW9onlsXE96dYywHU35IC0kNWghUXWRk7GZ3M/+xKBDnyPAhsiLCR99Pz2TxiAu/z5HJWN/MbNX7OG91dmUVlYxrm9Hpo5JoF9MpO1oyodpIalBC4mqj/w96exe8Cx98xcQLsfJCOjBwT6T6X/5ZMLCW9uOV2fllW4Wbd3H7BVZrNh1iOAAF1cP7MQDF/UgIbqV7XjKD2ghqUELiToXR4sOs3XhK3TY9hZx7mxKTAhbI0YRMPAm+lxwHSEhvtefUOU2rMw8yKcb8li4OY/DJRV0aduCW4bGcVNyLO3CQ2xHVH5EC0kNWkhUQxi3m+2rF1O46l16HvyS1hzlCOHsjEjBJF5G95TxtI3qaC1fcWkFK3Yd4psdBSzcso+C4jLCggO4tHcHJiTFcGFiFC69O686B1pIatBCohpLRXkpad9+RNnGD+lRuJy2FFNlhPSg8yhqP4Sgbil06jeGDp09dxHfgaNlbMopZH32EZbvPMjaPYepdBvCggMYnRjFNQM7c3GvaD2FVzWYFpIatJAoT6iqrGTnhm84sn4BrfatoHv5DkKkAoD9tGV/aDxHWyXgbn8eLaPjadOhK+FRcUS0iSIw8PQ/8lVuQ3FpBYdLKsg5XELWwRL2HCph94FjbMktYu+R4wCIQL/OkVyQ2J4LEqNIimtNyBk+V6n6atKFRETGAS8AAcCrxphnzjS/FhLlDaXHS8jasoyiHd9j8rfS+mgGsZXZhEnZD+arMkIJoZRK9aOSAF4I+gmrXQMpKq3gaFklJ/8TDQ500aVNC3p3imBgbGv6x0bSLyaS8JBAL35D1dycqZD49d88EQkApgNjgRxgtYjMN8ZstZtMNXehLcLomXwpJF/63zbjruJg3m72793F0YI9mKI8OHYAd/kxKC8h2H0cF1XEt+tIQIt2tAoNJKJFEJEtgmjdIoiYNi2IaxdGh1ah2s+hfIpfFxJgKJBhjNkFICJzgfGAFhLlc8QVQLuYHrSL6XHG+QZ5J45Sjca/r6yCGCC7xvscp+0HRGSKiKSKSGpBQYHXwimlVHPg74Wktv37Uzp9jDEzjTHJxpjkqKgoL8RSSqnmw98LSQ7Qpcb7WCDXUhallGqW/L2QrAYSRaSbiAQDE4H5ljMppVSz4ted7caYShF5EPiC6tN/XzfGbLEcSymlmhW/LiQAxph/A/+2nUMppZorfz+0pZRSyjItJEoppRrE72+RUl8iUgBknePi7YEDjRinsWiu+tFc9eer2TRX/TQkV5wxptbrJ5pdIWkIEUk93b1mbNJc9aO56s9Xs2mu+vFULj20pZRSqkG0kCillGoQLST1M9N2gNPQXPWjuerPV7NprvrxSC7tI1FKKdUgukeilFKqQbSQKKWUahAtJLUQkXEisl1EMkTk8Vqmi4hMc6ZvFJEkH8l1kYgUish65/G/Xsr1uojsF5HNp5lua3udLZfXt5eIdBGRpSKSJiJbROSntczj9e1Vx1w2tleoiKwSkQ1OrqdqmcfG9qpLLiv/Hp11B4jIOhFZUMu0xt9exhh91HhQffPHnUB3IBjYAPQ5aZ4rgc+pHg8lBVjpI7kuAhZY2GajgSRg82mme3171TGX17cX0AlIcl63Anb4yN+vuuSysb0ECHdeBwErgRQf2F51yWXl36Oz7keAd2tbvye2l+6RnOq/w/caY8qBE8P31jQeeMtUWwG0FpFOPpDLCmPMN8ChM8xiY3vVJZfXGWPyjDFrndfFQBqnjurp9e1Vx1xe52yDo87bIOdx8hlCNrZXXXJZISKxwFXAq6eZpdG3lxaSU9Vl+N46DfFrIRfAcGd3+3MR6evhTHVlY3vVlbXtJSLxwGCq/zdbk9XtdYZcYGF7OYdp1gP7gcXGGJ/YXnXIBXb+fj0PPAa4TzO90beXFpJT1WX43joN8dvI6rLOtVTfD2cg8A/gYw9nqisb26surG0vEQkHPgB+ZowpOnlyLYt4ZXudJZeV7WWMqTLGDKJ6BNShItLvpFmsbK865PL69hKRq4H9xpg1Z5qtlrYGbS8tJKeqy/C9Nob4Pes6jTFFJ3a3TfU4LUEi0t7DuerCJ4dEtrW9RCSI6h/rd4wxH9Yyi5XtdbZctv9+GWOOAP8Bxp00yerfr9PlsrS9RgLXishuqg9/Xywis0+ap9G3lxaSU9Vl+N75wCTn7IcUoNAYk2c7l4h0FBFxXg+l+s/3oIdz1YWN7XVWNraXs77XgDRjzHOnmc3r26suuSxtrygRae28bgFcCmw7aTYb2+usuWxsL2PME8aYWGNMPNW/EV8ZY247abZG315+P0JiYzOnGb5XRO5zpv+T6hEZrwQygBLgTh/JdSNwv4hUAseBicY5TcOTRGQO1WeotBeRHOBJqjsfrW2vOuaysb1GArcDm5zj6wC/ArrWyGVje9Ull43t1QmYJSIBVP8Qv2+MWWD732Mdc1n591gbT28vvUWKUkqpBtFDW0oppRpEC4lSSqkG0UKilFKqQbSQKKWUahAtJEoppRpEC4lSPkBEJoiIEZFetrMoVV9aSJTyDTcD31F9EZlSfkWvI1HKMuf+VtuBMcB8Y4zulSi/onskStl3HbDQGLMDONQoAw0p5UVaSJSy72aqb7CH83yzxSxK1Zse2lLKIhFpR/XdWPdTfSvvAOc5ztZ9mZSqL90jUcquG6kerS7OGBNvjOkCZAKjLOdSqs60kChl183ARye1fQDcYiGLUudED20ppZRqEN0jUUop1SBaSJRSSjWIFhKllFINooVEKaVUg2ghUUop1SBaSJRSSjWIFhKllFIN8v8Bj5jGWnvbEaAAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "b=-20\n", + "loss_values = np.zeros(y.shape)\n", + "a_values = np.zeros(y.shape)\n", + "for i,a in enumerate(np.linspace(0,4,y.shape[0])):\n", + " a_values[i][0] = a\n", + " y_pred= a*x+b\n", + " loss_values[i][0] = loss(y_pred,y)\n", + "\n", + "def derivative(a,b,x,y):\n", + " return (2*a*np.sum(x**2)+2*b*np.sum(x)-2*np.sum(x*y))/x.shape[0]\n", + "\n", + "step_count = 700\n", + "learning_rate = 0.0000025\n", + "#learning_rate = 0.00025\n", + "# Define initial value\n", + "a=0.5\n", + "loss_history = np.zeros((step_count+1,1))\n", + "a_history = np.zeros((step_count+1,1))\n", + "loss_history[0][0] = loss(a*x+b,y)\n", + "a_history[0][0] = a\n", + "for step in range(step_count):\n", + " a -= learning_rate*derivative(a,b,x,y)\n", + " loss_history[step+1][0] = loss(a*x+b,y)\n", + " a_history[step+1][0] = a\n", + " \n", + "\n", + "plt.xlabel('A')\n", + "plt.ylabel('Loss')\n", + "plt.plot(a_values,loss_values,a_history,loss_history)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2ARWloSLPLFc" + }, + "source": [ + "Our robot can succesfully converge to minimum point in this simple environment." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hagy6MT10rcK" + }, + "source": [ + "**Exercise**
\n", + "By using the code snippet above, change learning rate. Explain what problems may occur by setting a non-suitable learning rate.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ba-PKGjn1kUx" + }, + "source": [ + "Answer Here: When we increase the learning rate, our steps are getting bigger than the optimal value. In contrast, it will take enormous time to get to the bottom due to our steps are too small." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ImygonB_x_4A" + }, + "source": [ + "However, we have 2 variables in this problem, so we need to consider both values. While we can visualize 1 variable and loss with a simple 2D graph, we need to observe a 3D surface in order to get the grasp of the situation." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "fNorMl3lx_4B", + "outputId": "170751f6-c21a-456b-f1c6-d8be51ef3d75" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "loss_values = np.zeros((y.shape[0],y.shape[0]))\n", + "a_values = np.zeros((y.shape[0],y.shape[0]))\n", + "b_values = np.zeros((y.shape[0],y.shape[0]))\n", + "\n", + "for i,a in enumerate(np.linspace(0,4,y.shape[0])):\n", + " for j,b in enumerate(np.linspace(-80,40,y.shape[0])):\n", + " a_values[i][j] = a\n", + " b_values[i][j] = b\n", + " y_pred= a*x+b\n", + " loss_values[i][j] = loss(y_pred,y)\n", + "\n", + "cmap = plt.get_cmap('inferno')\n", + "fig, ax = plt.subplots()\n", + "im = ax.pcolormesh(a_values, b_values, loss_values, cmap=cmap)\n", + "ax.set_ylabel('B')\n", + "ax.set_xlabel('A')\n", + "cbar = fig.colorbar(im, ax=ax)\n", + "cbar.set_label('Loss')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jnDs87Scx_4B" + }, + "source": [ + "Now we neet to perform gradient descent on this 3D surface. \n", + "$$L=\\frac{1}{N}\\sum \\left( Ax_i+B-y_i \\right)^2$$ \n", + "$$\\frac{\\partial L}{\\partial B}=\\frac{1}{N}\\sum \\left[2\\left( Ax_i+B-y_i \\right)\\right]$$\n", + "$$\\frac{\\partial L}{\\partial B}=\\frac{1}{N}\\left[2A\\sum x_i + 2B\\times N - 2\\sum y_i\\right]$$ " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "FS6d73O4x_4C", + "outputId": "d734eedd-94a7-4bbf-fbe9-551807363794" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "loss_values = np.zeros((y.shape[0],y.shape[0]))\n", + "a_values = np.zeros((y.shape[0],y.shape[0]))\n", + "b_values = np.zeros((y.shape[0],y.shape[0]))\n", + "\n", + "for i,a in enumerate(np.linspace(0,4,y.shape[0])):\n", + " for j,b in enumerate(np.linspace(-80,40,y.shape[0])):\n", + " a_values[i][j] = a\n", + " b_values[i][j] = b\n", + " y_pred= a*x+b\n", + " loss_values[i][j] = loss(y_pred,y)\n", + "\n", + "cmap = plt.get_cmap('inferno')\n", + "fig, ax = plt.subplots()\n", + "im = ax.pcolormesh(a_values, b_values, loss_values, cmap=cmap)\n", + "ax.set_ylabel('B')\n", + "ax.set_xlabel('A')\n", + "cbar = fig.colorbar(im, ax=ax)\n", + "cbar.set_label('Loss')\n", + "\n", + "def derivative(a,b,x,y):\n", + " der_a = (2*a*np.sum(x**2)+2*b*np.sum(x)-2*np.sum(x*y))/x.shape[0]\n", + " der_b = (2*a*np.sum(x)-2*np.sum(y))/x.shape[0] + 2*b\n", + " return der_a, der_b\n", + "\n", + "step_count = 10\n", + "learning_rate = 0.00025\n", + "# Define initial value\n", + "a=0.5\n", + "b=-70\n", + "a_history = np.zeros((step_count+1,1))\n", + "b_history = np.zeros((step_count+1,1))\n", + "a_history[0][0] = a\n", + "b_history[0][0] = b\n", + "for step in range(step_count):\n", + " der_a , der_b = derivative(a,b,x,y)\n", + " a -= learning_rate*der_a\n", + " b -= learning_rate*der_b\n", + " a_history[step+1][0] = a\n", + " b_history[step+1][0] = b\n", + "\n", + "ax.plot(a_history,b_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wjt_BODwx_4C" + }, + "source": [ + "As it is observed, when same learning rate is applied one of the variables does not converge. If we change the learning rate we can observe algortihm finds the minimum point." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "l0qcIrYVx_4C", + "outputId": "c90c3a07-79c0-44ad-fe25-6503ac138342" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "loss_values = np.zeros((y.shape[0],y.shape[0]))\n", + "a_values = np.zeros((y.shape[0],y.shape[0]))\n", + "b_values = np.zeros((y.shape[0],y.shape[0]))\n", + "\n", + "for i,a in enumerate(np.linspace(0,4,y.shape[0])):\n", + " for j,b in enumerate(np.linspace(-80,40,y.shape[0])):\n", + " a_values[i][j] = a\n", + " b_values[i][j] = b\n", + " y_pred= a*x+b\n", + " loss_values[i][j] = loss(y_pred,y)\n", + "\n", + "cmap = plt.get_cmap('inferno')\n", + "fig, ax = plt.subplots()\n", + "im = ax.pcolormesh(a_values, b_values, loss_values, cmap=cmap)\n", + "ax.set_ylabel('B')\n", + "ax.set_xlabel('A')\n", + "cbar = fig.colorbar(im, ax=ax)\n", + "cbar.set_label('Loss')\n", + "\n", + "def derivative(a,b,x,y):\n", + " der_a = (2*a*np.sum(x**2)+2*b*np.sum(x)-2*np.sum(x*y))/x.shape[0]\n", + " der_b = (2*a*np.sum(x)-2*np.sum(y))/x.shape[0] + 2*b\n", + " return der_a, der_b\n", + "\n", + "step_count = 20\n", + "learning_rate = 0.00025\n", + "# Define initial value\n", + "a=0.5\n", + "b=-70\n", + "a_history = np.zeros((step_count+1,1))\n", + "b_history = np.zeros((step_count+1,1))\n", + "a_history[0][0] = a\n", + "b_history[0][0] = b\n", + "for step in range(step_count):\n", + " der_a , der_b = derivative(a,b,x,y)\n", + " a -= learning_rate*der_a\n", + " b -= learning_rate*der_b*1000\n", + " a_history[step+1][0] = a\n", + " b_history[step+1][0] = b\n", + "\n", + "ax.plot(a_history,b_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nvUpWMCBx_4D" + }, + "source": [ + "However, defining different learning rates for different variables is not an efficient solution. Instead, we use normalization on the dataset. By doing so, we achieve a uniform gradient map. Normailization is the process of fitting input and output variables in range of 0-1. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "wTDcQ9xMx_4D" + }, + "outputs": [], + "source": [ + "\n", + "x_min = x.min()\n", + "x_max = x.max()\n", + "y_min = y.min()\n", + "y_max = y.max()\n", + "\n", + "x = (x-x_min)/(x_max-x_min)\n", + "y = (y-y_min)/(y_max-y_min)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "DFiyoyg0x_4D", + "outputId": "e78dd26d-9f30-4e8c-974f-c528f2899c25" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "loss_values = np.zeros((y.shape[0],y.shape[0]))\n", + "a_values = np.zeros((y.shape[0],y.shape[0]))\n", + "b_values = np.zeros((y.shape[0],y.shape[0]))\n", + "\n", + "for i,a in enumerate(np.linspace(0,2,y.shape[0])):\n", + " for j,b in enumerate(np.linspace(-1,1,y.shape[0])):\n", + " a_values[i][j] = a\n", + " b_values[i][j] = b\n", + " y_pred= a*x+b\n", + " loss_values[i][j] = loss(y_pred,y)\n", + "\n", + "cmap = plt.get_cmap('inferno')\n", + "fig, ax = plt.subplots()\n", + "im = ax.pcolormesh(a_values, b_values, loss_values, cmap=cmap)\n", + "ax.set_ylabel('B Normalized')\n", + "ax.set_xlabel('A Normalized')\n", + "cbar = fig.colorbar(im, ax=ax)\n", + "cbar.set_label('Loss')\n", + "\n", + "def derivative(a,b,x,y):\n", + " der_a = (2*a*np.sum(x**2)+2*b*np.sum(x)-2*np.sum(x*y))/x.shape[0]\n", + " der_b = (2*a*np.sum(x)-2*np.sum(y))/x.shape[0] + 2*b\n", + " return der_a, der_b\n", + "\n", + "step_count = 10\n", + "learning_rate = 0.5\n", + "# Define initial value\n", + "a = 1\n", + "b= -0.9\n", + "a_history = np.zeros((step_count+1,1))\n", + "b_history = np.zeros((step_count+1,1))\n", + "a_history[0][0] = a\n", + "b_history[0][0] = b\n", + "for step in range(step_count):\n", + " der_a , der_b = derivative(a,b,x,y)\n", + " a -= learning_rate*der_a\n", + " b -= learning_rate*der_b\n", + " a_history[step+1][0] = a\n", + " b_history[step+1][0] = b\n", + "\n", + "ax.plot(a_history,b_history)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rtf6ghk0x_4E" + }, + "source": [ + "Figure above shows that, when the datapoints are in range of 0-1, more uniform gradient is achieved and gradient descent performs better." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gyf-CdBIyJaD" + }, + "source": [ + "**Exercise**
\n", + "There is another feature scaling method called standardization. Learn and explain what standardization is and how it compares versus normalization.
\n", + "Repeat the process above using standardization instead of normalization." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oQcFRlhuymdh" + }, + "source": [ + "Answer Here" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "E40frkxUG3YX" + }, + "outputs": [], + "source": [ + "'''standardization refers to the process of putting \n", + "different variables on the same scale in order to compare scores \n", + "between different types of variables.\n", + "When we use the standardization,we get better results than the normalization\n", + "Because if we set our max datapoint as 1 and other data points are far from \n", + "the max value, there will be an unclear data process. \n", + "'''\n", + "x_mean = x.mean()\n", + "x_std = x.std()\n", + "y_mean = y.mean()\n", + "y_std = y.std()\n", + "\n", + "x = (x-x_mean)/x_std\n", + "y = (y-y_mean)/y_std" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "loss_values = np.zeros((y.shape[0],y.shape[0]))\n", + "a_values = np.zeros((y.shape[0],y.shape[0]))\n", + "b_values = np.zeros((y.shape[0],y.shape[0]))\n", + "\n", + "for i,a in enumerate(np.linspace(0,2,y.shape[0])):\n", + " for j,b in enumerate(np.linspace(-1,1,y.shape[0])):\n", + " a_values[i][j] = a\n", + " b_values[i][j] = b\n", + " y_pred= a*x+b\n", + " loss_values[i][j] = loss(y_pred,y)\n", + "\n", + "cmap = plt.get_cmap('inferno')\n", + "fig, ax = plt.subplots()\n", + "im = ax.pcolormesh(a_values, b_values, loss_values, cmap=cmap)\n", + "ax.set_ylabel('B Standardized')\n", + "ax.set_xlabel('A Standardized')\n", + "cbar = fig.colorbar(im, ax=ax)\n", + "cbar.set_label('Loss')\n", + "\n", + "def derivative(a,b,x,y):\n", + " der_a = (2*a*np.sum(x**2)+2*b*np.sum(x)-2*np.sum(x*y))/x.shape[0]\n", + " der_b = (2*a*np.sum(x)-2*np.sum(y))/x.shape[0] + 2*b\n", + " return der_a, der_b\n", + "\n", + "step_count = 10\n", + "learning_rate = 0.5\n", + "# Define initial value\n", + "a = 1\n", + "b= -0.9\n", + "a_history = np.zeros((step_count+1,1))\n", + "b_history = np.zeros((step_count+1,1))\n", + "a_history[0][0] = a\n", + "b_history[0][0] = b\n", + "for step in range(step_count):\n", + " der_a , der_b = derivative(a,b,x,y)\n", + " a -= learning_rate*der_a\n", + " b -= learning_rate*der_b\n", + " a_history[step+1][0] = a\n", + " b_history[step+1][0] = b\n", + "\n", + "ax.plot(a_history,b_history)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2-ddvoNhx_4E" + }, + "source": [ + "## Load Boston house data and observe the parameters and linear correlation between them " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "_hpq_uCYx_4E" + }, + "outputs": [], + "source": [ + "\n", + "from sklearn.linear_model import SGDRegressor #gradient descent regression\n", + "from sklearn.datasets import load_boston # taking the data\n", + "from sklearn.datasets import make_regression # \n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.preprocessing import scale\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "ZUy9r3ycx_4E", + "outputId": "c5e2a696-c3fe-422e-bb93-89d30063850f", + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Boston housing dataset prediction.\n", + ".. _boston_dataset:\n", + "\n", + "Boston house prices dataset\n", + "---------------------------\n", + "\n", + "**Data Set Characteristics:** \n", + "\n", + " :Number of Instances: 506 \n", + "\n", + " :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n", + "\n", + " :Attribute Information (in order):\n", + " - CRIM per capita crime rate by town\n", + " - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n", + " - INDUS proportion of non-retail business acres per town\n", + " - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", + " - NOX nitric oxides concentration (parts per 10 million)\n", + " - RM average number of rooms per dwelling\n", + " - AGE proportion of owner-occupied units built prior to 1940\n", + " - DIS weighted distances to five Boston employment centres\n", + " - RAD index of accessibility to radial highways\n", + " - TAX full-value property-tax rate per $10,000\n", + " - PTRATIO pupil-teacher ratio by town\n", + " - B 1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\n", + " - LSTAT % lower status of the population\n", + " - MEDV Median value of owner-occupied homes in $1000's\n", + "\n", + " :Missing Attribute Values: None\n", + "\n", + " :Creator: Harrison, D. and Rubinfeld, D.L.\n", + "\n", + "This is a copy of UCI ML housing dataset.\n", + "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n", + "\n", + "\n", + "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n", + "\n", + "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n", + "prices and the demand for clean air', J. Environ. Economics & Management,\n", + "vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n", + "...', Wiley, 1980. N.B. Various transformations are used in the table on\n", + "pages 244-261 of the latter.\n", + "\n", + "The Boston house-price data has been used in many machine learning papers that address regression\n", + "problems. \n", + " \n", + ".. topic:: References\n", + "\n", + " - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n", + " - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n", + "\n", + "\n", + "\n", + "\n", + "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])\n", + "\n", + "\n", + "(506, 13)\n", + "\n", + "\n", + "['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'\n", + " 'B' 'LSTAT']\n" + ] + } + ], + "source": [ + "print(\"Boston housing dataset prediction.\")\n", + "boston = load_boston()\n", + "\n", + "print(boston.DESCR)\n", + "\n", + "print(type(boston))\n", + "print('\\n')\n", + "print(boston.keys())\n", + "print('\\n')\n", + "print(boston.data.shape)\n", + "print('\\n')\n", + "print(boston.feature_names)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "QBrs0YbPx_4F", + "outputId": "b149dd61-94e2-49cf-bfc7-1c7581d07ce2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data Feature Training size (506, 13)\n", + "Data Output size (506,)\n" + ] + } + ], + "source": [ + "x, y = boston.data, boston.target\n", + "print('Data Feature Training size',x.shape)\n", + "print('Data Output size',y.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "japQJflAGDOP" + }, + "source": [ + "### Let`s look at the correlation matrix for this data \n", + " Question: name the the best direct and inverse linear correlation to PRICE " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1fDkQUEoG7r7" + }, + "source": [ + "Answer here" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "best direct -> RM\n", + "best inverse ->LSTAT" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "51Q_V1RUx_4F", + "outputId": "7a95e303-1d21-4935-e880-c6f6ed457f0e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "bos_data = pd.DataFrame(boston.data, columns = boston.feature_names)\n", + "bos_data['PRICE']=boston.target\n", + "\n", + "sns.set(rc = {'figure.figsize':(15,8)})\n", + "sns.heatmap(data=bos_data.corr(), annot=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oIr_T1xNx_4F" + }, + "source": [ + "## Preprocessing \n", + "### Splitting the data into training and testing sets\n", + "Since we need to test our model, we split the data into training and testing sets. We train the model with 75% of the samples and test with the remaining 25%. We do this to assess the model’s performance on unseen data.\n", + "\n", + "Question: What does random_state do? and try to run the code without applying scale() or StandardScaler(), you will see that the algorithm will have a hard time to converge to a solution as explained above. In this following example, we use standardization but feel free to try it with normalization too.
\n", + "*Hint: Check the official documentation.*" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SigO_P1vHBst" + }, + "source": [ + "Answer Here : Random state helps us to splite our data in randomly, every time we assign random state to spesific number, it turns back the same datas as trains and tests." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "Ymu7VsKex_4G", + "outputId": "0593ffbe-cadc-4962-e700-9cb6d5c1e9c3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(379, 13)\n", + "(127, 13)\n", + "(379,)\n", + "(127,)\n", + "(379, 13)\n", + "(127, 13)\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.25, random_state=8)\n", + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "print(Y_train.shape)\n", + "print(Y_test.shape)\n", + "\n", + "\n", + "s=StandardScaler()\n", + "train_data=s.fit_transform(X_train)\n", + "test_data=s.transform(X_test)\n", + "#train_data = X_train\n", + "#test_data = X_test\n", + "print(train_data.shape)\n", + "print(test_data.shape)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7hWOQBWrx_4G" + }, + "source": [ + "## Set up SGD_Regressor hyperparameters \n", + "We have fitted multivariate linear regression model to the Boston House data. See SGDRegDefault.coef_ below, you will realize 13 parameters defining this equation\n", + "$$y=a_1x_1+a_2x_2+a_3x_3+a_4x_4+a_5x_5+a_6x_6+a_7x_7+a_8x_8+a_9x_9+a_{10}x_{10}+a_{11}x_{11}+a_{12}x_{12}+a_{13}x_{13}+b.$$\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "fdFZ1HBjx_4G", + "outputId": "b9b81bb4-6976-4de7-a4f1-4de95b421b55" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R-squared_train_forDefaultParameters: 0.7419979342810203\n", + "R-squared_test_forDefaultParameters: 0.7136998104813453\n" + ] + } + ], + "source": [ + "\n", + "from sklearn import linear_model\n", + "\n", + "SGDRegDefault = linear_model.SGDRegressor(max_iter=20) #all parameters are default\n", + "SGDRegDefault.fit(train_data, Y_train)\n", + "\n", + "#SGDReg =linear_model.SGDRegressor(\n", + "# max_iter = 1000,penalty = \"l1\",loss = 'huber',tol = 1e-3, alpha=0.0001 ,learning_rate='optimal' ,average = True)\n", + "#SGDReg.fit(train_data, Y_train)\n", + "\n", + "\n", + "\n", + "\n", + "score = SGDRegDefault.score(train_data, Y_train)\n", + "print(\"R-squared_train_forDefaultParameters:\", score)\n", + "#score = SGDReg.score(train_data, Y_train)\n", + "#print(\"R-squared_train:\", score)\n", + "\n", + "\n", + "score = SGDRegDefault.score(test_data, Y_test)\n", + "print(\"R-squared_test_forDefaultParameters:\", score)\n", + "#score = SGDReg.score(test_data, Y_test)\n", + "#print(\"R-squared_test:\", score)\n", + "\n", + "#mse = mean_squared_error(ytest, ypred)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "do0yBg1Zx_4H", + "outputId": "8ec6babc-a8c8-4016-8016-0577f96c9228" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.87219444, 0.96019373, -0.11246476, 0.7186035 , -1.1755924 ,\n", + " 2.75654638, -0.19597691, -2.83295018, 1.47452607, -0.86683883,\n", + " -1.82966752, 0.86160392, -3.98542973])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SGDRegDefault.coef_" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "1Bvwj6Owx_4H", + "outputId": "2fd59f8d-5dd5-42ef-8ddf-5a709a36be87", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Y_predict=SGDRegDefault.predict(test_data)\n", + "\n", + "\n", + "fig = plt.figure()\n", + "plt.scatter(Y_test,Y_predict)\n", + "plt.xlabel('Real')\n", + "plt.ylabel('Predicted')\n", + "plt.xlim(0,60)\n", + "plt.ylim(0,60)\n", + "plt.plot([0,60],[0,60],'r')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(bos_data[['DIS']],bos_data[['PRICE']])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(bos_data[['LSTAT']],bos_data[['PRICE']])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oaVBOn1wJBTc" + }, + "source": [ + " Question: Compare this coefficient values to the correlation matrix above, what is the coeffients for RM and LSTAT. \n", + "\n", + "\n", + "Question: Default mode in a function uses some set hyperparameters. However, a function is never limited with those hyperparameters. Check out to sklearn.linear_model.SGDRegressor see the parameters on sciket learn website. \n", + "\n", + "Device your own function by appropriately changing hyperparameters. Try to justify why you think as a set of hyperparameters works better if you can a better one than the default." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QUNMosF4JfSM" + }, + "source": [ + "Answer Here: 2.78607766 and -4.01178808" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FCaoKFrMx_4H" + }, + "source": [ + "# Nonlinear SGD \n", + "\n", + "The more complex your equation is, the more sensitive your analysis becomes for the hyperparameters. When we increased the multivariate polynomial degree to 2, the R2 became negative infinity in this case. Apparently, the algorithm cannot find a local min and gets lost." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "aaudhmAyx_4H", + "outputId": "b77322c9-c76d-48cd-c770-4f13736140f4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R-squared_train: -1.267983145585444e+19\n", + "R-squared_test: -1.8200218379893007e+18\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import PolynomialFeatures\n", + "\n", + "####\n", + "N_poly_degree=2\n", + "####\n", + "\n", + "poly = PolynomialFeatures(N_poly_degree)\n", + "\n", + "train_data_poly=poly.fit_transform(train_data)\n", + "test_data_poly=poly.fit_transform(test_data)\n", + "\n", + "SGDRegDefault = linear_model.SGDRegressor(max_iter=100) #all parameters are default\n", + "SGDRegDefault.fit(train_data_poly, Y_train)\n", + "\n", + "\n", + "\n", + "score_train = SGDRegDefault.score(train_data_poly, Y_train)\n", + "print(\"R-squared_train:\", score_train)\n", + "\n", + "score_test = SGDRegDefault.score(test_data_poly, Y_test)\n", + "print(\"R-squared_test:\", score_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "x0UcQxMix_4H", + "outputId": "5d3754a8-eb37-4959-cd88-3410a0562a41" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(105,)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SGDRegDefault.coef_.shape\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "ccRgvNMXx_4I", + "outputId": "0a92faf3-732a-497b-e826-acc6005f782f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Y_predict=SGDRegDefault.predict(test_data_poly)\n", + "\n", + "fig = plt.figure()\n", + "plt.scatter(Y_test,Y_predict)\n", + "plt.xlabel('Real')\n", + "plt.ylabel('Predictions')\n", + "plt.xlim(0,60)\n", + "plt.ylim(0,60)\n", + "plt.plot([0,60],[0,60],'r')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nCjUQWhhJt7j" + }, + "source": [ + " Question: Check out SGDRegDefault.coef_ and compare it to the linear model where there are 13 coefficients for 13 parameters \n", + " \n", + "Why do you think that number of coefficients is so drastically increased. Give a few examples of terms in this second degree nonlinear example. \n", + "\n", + " Question: Use various hyperparameters and loss evaluations to converge to a solution. There is a solution found by us for polynomial degree two. You can read the SGDRegressor hyperparameter definitions to make educated guesses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First Answer:\n", + "when we increaase the polynomial degree of our function to 2nd degree. We got new hyperparameters which composed of first 13 parameters.Number of hyperparameters are related to bynomial distribution of (a1x1 + a2x2+..+ a13x13+b)^2 its element count is equal to (our parmeter number is the 13 parameter + b as 14) 14*(14+1) = 105" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second Answer:\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R-squared_train: 0.42441102599626834\n", + "R-squared_test: 0.44713239281628336\n" + ] + } + ], + "source": [ + "SGDReg =linear_model.SGDRegressor(max_iter = 1000,penalty = \"l1\",loss = 'huber',tol = 1e-3, alpha=0.0001 ,learning_rate='optimal' ,average = True)\n", + "SGDReg.fit(train_data, Y_train)\n", + "\n", + "\n", + "\n", + "score = SGDReg.score(train_data, Y_train)\n", + "print(\"R-squared_train:\", score)\n", + "\n", + "score = SGDReg.score(test_data, Y_test)\n", + "print(\"R-squared_test:\", score)\n", + "\n", + "#mse = mean_squared_error(Y_test, Y_pred)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R-squared_train: -1.267983145585444e+19\n", + "R-squared_test: -1.8200218379893007e+18\n" + ] + } + ], + "source": [ + "from sklearn.preprocessing import PolynomialFeatures\n", + "\n", + "####\n", + "N_poly_degree=2\n", + "####\n", + "\n", + "poly = PolynomialFeatures(N_poly_degree)\n", + "\n", + "train_data_poly=poly.fit_transform(train_data)\n", + "test_data_poly=poly.fit_transform(test_data)\n", + "\n", + "#SGDRegDefault = linear_model.SGDRegressor(max_iter=100) #all parameters are default\n", + "SGDReg =linear_model.SGDRegressor(max_iter = 1000,penalty = \"l1\",loss = 'huber',tol = 1e-3, alpha=0.0001 ,learning_rate='optimal' ,average = True)\n", + "SGDReg.fit(train_data_poly, Y_train)\n", + "\n", + "\n", + "\n", + "score_train = SGDRegDefault.score(train_data_poly, Y_train)\n", + "print(\"R-squared_train:\", score_train)\n", + "\n", + "score_test = SGDRegDefault.score(test_data_poly, Y_test)\n", + "print(\"R-squared_test:\", score_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(105,)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SGDReg.coef_.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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jGIZaPvpQx/7Pj3Tq//6rLFabhj2zQpf83Q+VcPU1FD4MmYCN9M2ePVt79uxRYWGh/H6/7r33Xk2ePFkrV67UihUr5Ha7lZeXp/nz5wcqAgAAAGCKtgP7VVeySh2HDykqM0s5jz+ppJumymIN2JgL0C2LYRghtUCONX3oDdZRoC+4XtBbXCvoC66XyNR+6AvVlaxS+8EDsqelKz1/iVKmzZDF3v1YC9cKeivo1vQBAAAAkaLj2FE5S4vVunePbEnJyrz7XqXkzZI1KtrsaAClDwAAAOgvz6mTqisrVcvuXbLGxyuj6A6l3jZH1pgYs6MB51D6AAAAgD7qrKuVs7xMTdU7ZImOUfrifKXNnS9bfILZ0YBvoPQBAAAAveRtqJdzzWo1vr1VFotFabfPVdrCRbInJZsdDegWpQ8AAAC4CF9zs1yVa9SweaMMv18pM25R+uIlikpLMzsacFGUPgAAAKAbvrY21W9Yp/r162R43EqeOk3pSwoUnZlldjSg1yh9AAAAwAX8brcaNr0lV+Va+dtalXj9DXIUFClm+HCzowF9RukDAAAA/szf2anGt7fItWa1fE1NSrjmWjmWLlPsJaPNjgb0G6UPAAAAEc/w+dS0c7ucq8vldTkVd8WVynhmheLGjTc7GjBglD4AAABELMPvV/N7u+QsL1Hn6dOKvWyMsh9+VPETrpLFYjE7HjAoKH0AAACIOIZhqPWjD1VXWizPia8UPXKUhn/7O0qYdB1lD2GH0gcAAICIYRiG2vbvU13JKrmPfKmo7GzlPLlcSTfcJIvVanY8ICAofQAAAIgI7Z9/rrqSP6r9s09lT3co++FHlZw7XRabzexoQEBR+gAAABDWOo4eUV1Jsdo+2StbSooy771fKTPzZI2KMjsaMCQofQAAAAhL7pMn5CwrUcv7u2VNSFDGsruUeuttssbEmB0NGFKUPgAAAIQVT+0ZOctL1fxOtawxMUrPL1DanHmyxcebHQ0wBaUPAAAAYaHT5ZJrTbkat2+TxWZT2tz5Sp+/ULakJLOjAaai9AEAACCkeZua5Kpco8bNG2UYhlJumSXHonzZU1PNjgYEBUofAAAAQpKvrVX166pU/9Z6GR6PkqfNkCN/iaIyMs2OBgQVSh8AAABCir+jQ/UbN6h+XaX8bW1KuvEmOQqWKjpnmNnRgKBE6QMAAEBI8Hd61Lhls1xr18jX3KSESdcpo7BIMaMuMTsaENQofQAAAAhqhterxh3b5aool7fepfgJV8lRWKS4sePMjgaEBEofAAAAgpLh96v53XfkLC9VZ+0ZxY4dp5zHnlD8lRPMjgaEFEofAAAAgophGGr54H05y0rkOXlCMaMu0fDn/koJ10ySxWIxOx4Qcih9AAAACAqGYaht38eqKymW++gRRecM07Dlzyhxyg2yWK1mxwNCFqUPAAAApmv77FM5S1ap/fPPZM/IUPYjjyt5aq4sNpvZ0YCQR+kDAACAaTq+PKy60mK17ftEtpRUZd33oFJm3iKLnZepwGDhfxMAAACGnPvEV6orLVbrhx/ImpiojDu/pdTZt8kaHW12NCDsUPoAAAAwZDynT8tZXqLmXe/KGhsrR8FSpc2ZK2tsnNnRgLBF6QMAAEDAdbqccq4uU9OO7bLY7Uqbv1Dp8xbIlphodjQg7FH6AAAAEDDexka51laocetmSVLq7NuUvnCR7Cmp5gYDIgilDwAAAIPO19Ii17pKNWzcIMPrVfL0GXIsLlCUw2F2NCDiUPoAAAAwaPwd7arfsF7166vk7+hQ0k03y7GkUNHZOWZHAyIWpQ8AAAAD5vd41LB5o+or18rX0qyEyVOUUbBUMSNHmR0NiHiUPgAAAPSb4fWqcdvbcq4pl6+hQfETr1ZGYZFiLxtjdjQAf0bpAwAAQJ8Zfr+aqnfKtbpMnXW1iht/uRxPPq34y68wOxqAC1D6AAAA0GuG36+WD3bLWVoiT80pxVwyWiP+6ruKn3iNLBaL2fEAdIHSBwAAgIsyDEOtH++Rs7RE7mNHFT18uIY9/awSp9xA2QOCHKUPAAAAPWo7eEB1JavUcegLRWVmKuexJ5V081RZrFazowHoBUofAAAAutR++JCcJavUdmC/7GlpynrgYaVMnyGLnZeQQCjhfywAAADO4z5+XHWlq9S65yPZkpKUedc9Spk9W9aoaLOjAegHSh8AAAAkSZ6aU3KWlaj5vV2yxsXJUViktNvnyhoba3Y0AANA6QMAAIhwnc46OcvL1LRzuyzR0UpflK+0ufNlS0gwOxqAQUDpAwAAiFDehgY516xW49tbZLFYlHr7XKUvWCR7crLZ0QAMIkofAABAhPG1tMhVuUYNmzfK8PmUMn2m0hcvUVR6utnRAAQApQ8AACBC+NrbVb++Sg0b1snvditpaq4c+YWKzsoyOxqAAKL0AQAAhDm/262GTRvlqlojf2urEqdcL0dBkWJGjDA7GoAhQOkDAAAIU/7OTjVu2yrXmtXyNTYq/uprlFG4TLGXXmp2NABDiNIHAAAQZgyfT03VO+RcXSav06m4y69QxvJnFTf+crOjATABpQ8AACBMGH6/mnfvkrOsVJ2naxRz6WXKfvARxV81URaLxex4AExC6QMAAAhxhmGodc9Hqistluer44oeMVLDn12hhOumUPYAUPoAAABCWduB/aor+aM6Dh9WVFa2cp54Skk33iyL1Wp2NABBgtIHIKJV76tR6fZq1da3y5Eco6K8scqdmGN2LAC4qPYvPlddabHaDx6QPT1d2Q8+ouRp02Wx8/IOwPn4rQAgYlXvq9FrlQfl8folSc4mt16rPChJFD8AQavj2FE5S1ap9eO9siUnK/Pu+5SSN0vWqCizowEIUpQ+ABGreOuhc4XvLI/Xr+Kthyh9AIKO++RJOcuK1fL+blnjE5RRdIdSb5sja0yM2dEABDlKH4CI5Wxy9+l2ADBDZ22tnKtL1VS9U5boGKUvXqK0ufNki08wOxqAEEHpAxCxHMkxXRY8RzLvmgMwX2d9vVwV5Wrc/rYsVqvS5sxT2oKFsiclmx0NQIih9AGIWEV5Y89b0ydJ0XarivLGmpgKQKTzNTfLVblGDZs3yvD7lTIzT47F+bKnppkdDUCIovQBiFhn1+2Vbv+S3TsBmM7X1qb69VWq37Behset5NxpcuQXKioz0+xoAEIcpQ9ARMudmKMls8artrbZ7CgAIpTf7VbDxg1yVVXK39aqxBtulGPJUsUMH252NABhgtIHAEAQq95Xo+Kth+RscjMaHWb8nR41bt0i15oK+ZqblHDtJDkKixR7yWizowEIM5Q+AACCFGdJhifD61XTzh1yVpTJ63Ip7soJylj6nOLGjjM7GoAwRekDACBIcZZkeDH8fjW/966cZaXqPHNasWPGKOeRxxU/4SqzowEIcwEvfT/72c9UX1+vlStXaufOnXrppZfkdru1YMECPf/884F+eAAAQhZnSYYHwzDU+tEHqistkefEV4oeOUrDv/0dJUy6ThaLxex4ACJAQEtfdXW1SkpKNGvWLHV0dOiFF17QG2+8oWHDhumpp57S1q1blZeXF8gIAACELM6SDG2GYah13yeqK1kl95EvFZWdo2FPPq3EG26UxWo1Ox6ACBKw3zgNDQ16+eWXtXz5cknS3r17NXr0aI0aNUp2u135+fmqqqoK1MMDABDyivLGKtp+/lM1Z0mGhvbPP9Mnf/dDnXj5n+RrblL2w4/p0n/4iZJuupnCB2DIBWyk74c//KGef/55nTp1SpJ05swZZX7tnJmsrCydPn26z/frcCQOWkaEt8zMJLMjIIRwvaC3hvJaWTIrSclJsXq98oDq6tuVkRanBxdM0KzrRw1ZBvRNyxeHdPT//bcaPvhQUWmpGvPk48qee7usUVFmR0OQ43kIgRSQ0veHP/xBw4YNU25uroqLiyVJfr//vHnrhmH0ax6709kiv98YtKwIT5mZSZy7hl7jekFvmXGtTLwkVT97Kve827heg4/7xAk5y4rV8sH7siYkKOOOuzTurkK5mjxyNnRI6jA7IoIYz0PoLavV0q9BsICUvrVr16q2tlYFBQVqbGxUW1ubTpw4IZvNdu5zamtrlZWVFYiHBwAAGBKeM2fkLC9R87vvyBoTI8eSQqXOmSdbXJxsMTGSPGZHBIDAlL7f/e535/5cXFysXbt26e///u81d+5cHT16VCNHjlRFRYWWLVsWiIcHAAAIqE6XS66KcjXu2CaLzaa0eQuUPn+hbIksQwEQfIbsnL6YmBitXLlSK1askNvtVl5enubPnz9UDw8AADBg3qYmudZWqHHLJhmGodS8WUpfmC97aqrZ0QCgWxbDMEJqgRxr+tAbzI1HX3C9oLe4ViKXr7VV9esqVb9xgwyPR8nTZ8iRX6AoR0a3X8P1gt7iWkFvBdWaPgAAgHDg7+hQ/VvrVb++Sv62NiXddLMcS5YqOifH7GgA0GuUPgAAgAv4Oz1q3LxZrsoK+ZqblXDdZGUUFClmFMdlAAg9lD4AAIA/M7xeNe7YJldFubz19YqfMFGOpUWKGzPW7GgA0G+UPgAAEPEMv1/N71bLWV6qztpaxY4dp5zHnlT8lRPMjgYAA0bpAwAAEcswDLV8sFvOshJ5Tp5UzCWjNfy555VwzbWyWCxmxwOAQUHpAwAAEccwDLV98rHqSlbJfeyooocN17DlzypxyvWyWK1mxwOAQUXpAwAAEaXt04OqK1mlji8+V1RGpnIefUJJU3MpewDCFqUPAABEhI4vD6uuZJXa9u+TLTVVWfc/qJQZt8hi5+UQgPDGbzkAYat6X42Ktx6Ss8ktR3KMivLGKnciZ2sBkcb91XHVlRar9aMPZUtMUuZddytl1q2yRkebHQ0AhgSlD0BYqt5Xo9cqD8rj9UuSnE1uvVZ5UJIofkCE8JyukbOsVM3vvStrbKwchUVKu32OrLFxZkcDgCFF6QMQloq3HjpX+M7yeP0q3nqI0geEuU6nU87VZWrauV0Wu13pCxYpbe582RITzY4GAKag9AEIS84md59uBxD6vI0Ncq2pUOPbWyRJqbfepvQFi2VPSTE3GACYjNIHICw5kmO6LHiO5BgT0gAIJF9Li1xVa9Ww6S0ZXq9SZsxU+uIlikp3mB0NAIICpQ9AWCrKG3vemj5JirZbVZQ31sRUAAaTv6Nd9RvWq359lfwdHUq6aaocSwoVnZ1tdjQACCqUPgBh6ey6PXbvBMKP3+NRw+aNclWukb+lRYmTr5ejcKliRow0OxoABCVKH4CwlTsxh5IHhBHD61Xjtq1yVqyWr7FB8ROvVsbSZYq99DKzowFAUKP0AQCAoGb4fGp6Z6ecq8vkratT3PjL5XjqacVffoXZ0QAgJFD6AABAUDL8frW8v1t1ZcXqrKlRzOhLlX3/Q4qfeLUsFovZ8QAgZFD6AABAUDEMQ61798hZWiz38WOKHj5Cw55ZocTJUyh7ANAPlD4AABA02g7sV11psToOfaGozCzlPP6kkm6aKovVanY0AAhZlD4AAGC69kNfqK5kldoPHpA9LV1ZDz6slGkzZLHzUgUABorfpAAAwDTu48dUV7JKrXv3yJaUpMxv3aOUWbNljYo2OxoAhA1KHwAEWPW+Gs4LBC7gqTmlutIStezeJWt8vDKK7lDqrbfLGhtrdjQACDuUPgAIoOp9NXqt8qA8Xr8kydnk1muVByWJ4oeI1FlXK2d5mZqqd8gSHa30xflKmztftvgEs6MBQNii9AFAABVvPXSu8J3l8fpVvPUQpQ8RxdvQIOeacjW+vVUWi0Vpt89V2sJFsiclmx0NAMIepQ8AAsjZ5O7T7QgdTNvtHV9zs1xVa9SwaaMMv18pM2YqfdESRaWnmx0NACIGpQ8AAsiRHNNlwXMkx5iQBoOFabsX52trU/2GdWrYsE5+t1vJU6cpfUmBojOzzI4GABGH0gcAAVSUN/a8ciBJ0XarivLGmpgKA8W03e753W41bNooV9Ua+VtblXj9DXIULFXM8BFmRwOAiEXpA4AAOlsAmAYYXpi2+03+zk41btsq15rV8jU2Kv7qa5WxtEixoy81OxoARDxKHwAEWO7EHEpemGHa7l8YPp+adm6Xc3W5vC6n4i6/QhnLv6248ePNjgYA+DNKHwAAfcS0Xcnw+9X83i45y0vUefq0Yi8bo+yHHlH8VRNlsVjMjgcA+BpKHwAAfRTJ03YNw1Drno9UV7JKnhNfKXrESA3/9neUMOk6yh4ABClKHwAA/RBp03YNw1Dbgf1ylqxSx5eHFZWdrZwnlivpxptksVrNjgcA6AGlDwAA9Kj9889VV/JHtX/2qezpDmU/9IiSp82QxWYzOxoAoBcofQAAoEsdx47KWbJKrR/vlS05WZn33KeUW2bJGhVldjQAQB9Q+gAAwHncJ0/KWVaslvd3yxqfoIxldyr11ttljYm83UkBIBxQ+gAgwlXvq4nIDUnwTZ7aM3KVl6npnZ2yRMcoPb9AaXPmyRYfb3Y0AMAAUPoAIIJV76s57+gBZ5Nbr1UelCSKXwTprK+Xq6JMjdu3yWK1Km3uPKXPXyRbUpLZ0QAAg4DSBwARrHjrofPOmpMkj9ev4q2HKH0RwNvcpPq1a9SweaMMw1DKLXlyLMqXPTXN7GgAgEFE6QOACOZscvfpdoQHX1ur6tdXqX7DBhket5Jzp8uxpEBRGZlmRwMABAClDwAimCM5psuC50hmw45w5O/oUMOmt+SqWit/W5sSb7hJGQWFih423OxoAIAAovQBQAQryht73po+SYq2W1WUN9bEVBhs/k6PGrdukWtNhXzNTUq4dpIchUWKvWS02dEAAEOA0gcAEezsuj127wxPhterxp3b5VpdLm+9S3FXTlDG0ucUN3ac2dEAAEOI0gcAES53Yg4lL8wYfr+ad70jZ1mpOmvPKHbMWOU8+rjiJ1xldjQAgAkofQAAhAnDMNTy4QdylhbLc/KEYkaN0vAVf6WEayfJYrGYHQ8AYJIeS98DDzzQ45PE66+/PuiBAABA3xiGobZ9n6iutFjuI18qKidHw556RonX3yCL1Wp2PACAyXosfffff78kacOGDWppadGyZctks9lUVlam5OTkIQkIAAC61/bZp3KWrFL755/JnpGh7EceU/LUabLYbGZHAwAEiR5L37x58yRJv/3tb/U///M/sv753cJZs2bpW9/6VuDTAQCALnUc+VJ1JavUtu8T2VJSlXXfA0qZmSeLnZUbAIDz9eqZob6+Xm63W3FxcZKk1tZWNTY2BjQYAAD4JveJE3KWFqvlw/dlTUxUxp3fUuqsW2WN4WxFAEDXelX6Fi9erLvuuktz5syRYRiqqqrSXXfdFehsAADgzzynT8tZXqrmXe/IGhsrR8FSpd4+V7Y/vyELAEB3elX6vvOd7+jqq69WdXW1JOn73/++8vLyAhoMAABInS6nXBXlaty+TRa7XWnzFih9/kLZEhPNjgYACBG9nvifmZmpcePGqaioSPv27QtkJgAAIp63sVGuygo1btksSUqddavSFy2WPSXV3GAAgJDTq9K3atUq/fu//7vcbrfmzJmjZ555Rs8//zxTPAEAGGS+1lbVr6tU/VvrZXi9Sp42Q478AkU5HGZHAwCEqF6Vvv/8z//Um2++qfvvv18Oh0PFxcV6/PHHKX0AAAwSf0e76t/aoPp1lfJ3dCjpxpvlWFKo6Jwcs6MBAEJcr0qf1WpV4tfWDgwbNkw2zv8BAGDA/B6PGrdskmvtGvlampVw3WRlFBYpZuQos6MBAMJEr0pfamqqDhw4IIvFIkkqLy9XSkpKQIMBABDODK9XjdvflrOiXL6GBsVfNVGOwmWKGzPG7GgAgDDTq9L3wgsv6Dvf+Y6OHTumGTNmKCYmRq+++mqgswEAEHYMv1/N71TLWV6qzrpaxY4br2FPLFf8FVeaHQ0AEKZ6VfrGjBmjsrIyHTlyRD6fT5dddpna2toCnQ0AgLBh+P1q+eB9OctK5Dl1UjGXjNaI73xX8Vdfc24mDQAAgdCr0ldUVKSSkhKNHTv23G333XefKioqAhYMAIBwYBiGWj/eK2dpsdzHjip62HANe/pZJU65gbIHABgSPZa+hx56SB9//LE6Ojo0ZcqUc7f7/X5dc801AQ8HAEAoazt4QHUlq9Rx6AtFZWYq57EnlHRzrixWq9nRAAARpMfS96tf/UoNDQ164YUX9NJLL/3li+x2ZWZmBjwcAAChqP3wYTlLVqntwD7Z09KU9cBDSpk+UxZ7rybYhLzqfTUq3npIzia3HMkxKsobq9yJHD0BAGbp8a3GxMREjRw5Uq+++qoqKio0YsQISdK//du/qaOjY0gCAgAQKtzHj+vEL3+h4z/9B7m/OqbMu+7RpT/5mVLzZkdU4Xut8qCcTW5JkrPJrdcqD6p6X43JyQAgcvXqGehv//ZvNXLkSElScnKyLBaLfvCDH+if//mfAxoOAIBQ4KmpkbO8RM3v7ZI1NlaOwiKl3T5X1thYs6MNueKth+Tx+s+7zeP1q3jrIUb7AMAkvSp9R44c0b/8y79IkpKSkvTCCy9oyZIlAQ0GAECw63TWybm6TE07d8gSFaX0BYuUNm+BbAkJZkczzdkRvt7eDgAIvF6VPq/Xq5aWFiUmJkqSWltbZRjGRb/uF7/4hdatWyeLxaI77rhDjzzyiHbu3KmXXnpJbrdbCxYs0PPPPz+wfwEAAEPM29gg15rVanx7qyQp9dbblb5wsezJySYnM58jOabLgudIjjEhDQBA6mXpKyws1J133qn58+fLYrFow4YNKioq6vFrdu3apXfeeUfl5eXyer1auHChcnNz9cILL+iNN97QsGHD9NRTT2nr1q3Ky8sblH8MAACB1NnUrNo//l4Nm96S4fMpZfpMpS/OV1S6w+xoQaMob6xeqzx43hTPaLtVRXlje/gqAEAg9ar0PfXUUxo3bpyqq6tlt9v113/91xctajfddJNef/112e12nT59Wj6fT01NTRo9erRGjRolScrPz1dVVRWlDwAQ1Hzt7WrYsE6H3lovX3u7km6eKseSpYrOyjI7WtA5u26P3TsBIHj0WPrOTulsaGjQ9ddfr+uvv/7c3zU0NCg1NbXHO4+KitIrr7yif//3f9f8+fN15syZ8456yMrK0unTp/sU2OFI7NPnI3JlZiaZHQEhhOsFXfG53Tq1plInikvlbW6WI/dmXXLv3Yq/5BKzowW1JbOStGTWeLNjBAV+t6C3uFYQSD2WvgceeEAlJSWaOnWqLBbLudsNw5DFYtGBAwcu+gDPPfecnnjiCS1fvlxHjhzp8n76wulskd9/8fWEiGyZmUmqrW02OwZCBNcLLmR4vWp8e4ucayrka2xQ/NXXaHjhMo268RrV1jarlesFvcDvFvQW1wp6y2q19GsQrMfSV1JSIkk6ePBgn+/40KFD8ng8mjBhguLi4jR37lxVVVXJZrOd+5za2lplMTUGABAkDJ9PTdU75VxdKq/Tqbjxl8vx1NOKv/wKs6MBANBvPZa+0tLSHr+4sLCw27/76quv9Morr+i///u/JUkbN27U3XffrZ///Oc6evSoRo4cqYqKCi1btqzPoQEAGEyG36+W3e+prrxEnTU1irn0MmU/8LDiJ17d5xkpAAAEmx5LX1VVlaQ/jcgdPnxYU6dOld1u17vvvqsJEyb0WPry8vK0d+9eFRYWymazae7cuVq0aJHS09O1YsUKud1u5eXlaf78+YP6DwIAoLcMw1Drno/kLCuW+/hxRY8YqeHPrlDCdVMoewCAsGExenHg3pNPPqkXX3xRl/x54frJkyf1gx/8QL/97W8DHvBCrOlDbzA3Hn3B9RKZ2g7sV13JKnUcPqSorGw5CgqVdOPNslit3X4N1wr6gusFvcW1gt4KyJq+s06dOnWu8EnS8OHDVVNT0+cHAwB0rXpfDVvcD5H2Q1+ormSV2g8ekD0tXdkPPqLkadNlsffqKREAgJDTq2e4zMxMvfLKK1q6dKkk6c033zx31h4AYGCq99Wcd5i1s8mt1yr/tIEWxW/wdBw7KmdpsVr37pEtKVmZd9+rlLxZskZFmx0NAICA6lXpW7lypf7+7/9eBQUFslqtmjlzpn76058GOhsARITirYfOFb6zPF6/irceovQNAs+pk6orK1HL7vdkjU9QRtEdSr1tjqwxMWZHAwBgSPSq9GVlZelXv/qVGhsblZKSEuhMABBRnE3uPt2O3umsrZVzdamaqnfKEh2j9MX5Sps7X7b4BLOjAQAwpHpV+g4fPqxvf/vbam5u1h//+Ec9/PDD+uUvf6mxY8cGOh8AhD1HckyXBc+RzEhUf3gb6uWsWK3GbVtlsViUdvtcpS1cJHtSstnRAAAwRfdblH3Nj3/8Y/3d3/2dHA6HsrOzdf/99+uHP/xhoLMBQEQoyhuraPv5v46j7VYV5fHGWl/4mptV+/v/0Zd/+z01btuqlBm36NKX/lGZ37qHwgcAiGi9GulraGjQ9OnT9Y//+I+SpPvuu0+///3vAxoMACLF2XV77N7ZP762NtWvr1L9hvUyPG4l506TI79QUZmZZkcDACAo9Hp/arfbfe6g2traWvn9/ot8BQCgt3In5lDy+sjvdqth4wa5qirlb2tV4vU3yFFQpJjhw82OBgBAUOlV6bvnnnv02GOPyel06p//+Z+1Zs0aPf7444HOBgDAN/g7O9X49ha51qyWr6lJCddcK8fSZYq9ZHSvvp4zEQEAkaZXpe/OO+/UpZdeqi1btsjr9er//J//o+nTpwc6GwAA5xg+n5p2bJezokxel0txV1ypjGdWKG7c+F7fB2ciAgAiUa9K30MPPaTXXntNN954Y6DzAABwHsPvV/N778pZVqrOM6cVe9kYZT/8mOInXHVu2UFvcSYiACAS9ar0NTc3q62tTfHx8YHOAwCAJMkwDLV+9KHqSovlOfGVokeO0vBvf0cJk67rc9k7izMRAQCRqFelLy4uTrNnz9YVV1xxXvH79a9/HbBgAIDIZBiG2vbvU13JKrmPfKmo7BzlPLlcSTfcJIu1VycNdYszEQEAkeiipe+zzz7TbbfdphkzZignh6kvAIDAaf/8M9WVrFL7Z5/Knu5Q9sOPKjl3uiw226Dcf1He2PPW9EmciQgACH89lr5Vq1bpZz/7mUaPHq1jx47pn/7pnzRz5syhygYAiBAdR4+orqRYbZ/slS0lRZn33q+UmXmyRkUN6uNwJiIAIBL1WPreeOMNrV69WtnZ2frwww/18ssvU/oAAIPGffKEnGUlanl/t6wJCcpYdpdSb71N1pjATbfkTEQAQKS56PTO7OxsSdLkyZNVX18f8EAAgPDnOXNGztWlan6nWtaYGKXnFyhtzjzZBrhhGGfwAQDwTT2Wvgt3R7MN0poKAEBk6nS55KooV+OObbLYbEqbO1/pCxbJlpg44PvmDD4AALrWq907z+rvFtkAgMjmbWqSq3KNGjdvlGEYSrlllhyL8mVPTR20x+AMPgAAutZj6fv00081ZcqUcx93dHRoypQpMgxDFotFH3zwQcADAgBCl6+tVfXrqlT/1noZHo+Sp82QI3+JojIyB/2xOIMPAICu9Vj6NmzYMFQ5AABhxN/RofqNG1S/rlL+tjYl3XiTHAVLFZ0zLGCPyRl8AAB0rcfSN2LEiKHKAQAIA/5Ojxq3bJZrbYV8zc1KmHSdMgqLFDPqkoA/NmfwAQDQtT6t6QMAoCuG16vGHdvlqiiXt96l+AlXyVFYpLix44YsA2fwAQDQNUofAIit/vvL8PvV/O47cpaXqrP2jGLHjlPOY08o/soJpuThDD4AAL6J0gcg4m15/zhb/feRYRhq+eB9OcuK5Tl5UjGjLtHw5/5KCddMYqdnAACCDKUPQMR7vfIAW/33kmEYatv3sepKiuU+ekTROcM0bPkzSpxygyxWq9nxAABAFyh9ACJeXX17l7ez1f/52j77VM6SVWr//DPZMzKU/cjjSp6aK4vNZnY0AADQA0ofgIiXkRan2i6KH1v9/0nHl4dVV1qstn2fyJaaqqz7HlTKzFtksfMUAgBAKOAZG0DEe3DBBP3L7z9iq/8LuE98pbrSYrV++IGsiYnKuPNbSp19m6zR0WZHAwAAfUDpAxDxZl0/Sk3NHeze+Wee06flLC9R8653ZY2NlaNgqdLmzJU1Ns7saGGL3WMBAIFE6QMAsdW/JHU6nXJWlKlpx3ZZ7HalzV+o9HkLZEtMNDtaWKveV8PusQCAgKL0AUCE8zY2yrW2Qo1bN0uSUmffpvSFi2RPSTU3WIQo3nqI3WMBAAFF6QOACOVraZFrXaUaNm6Q4fUqefoMORYXKMrhMDtaROlul1h2jwUADBZKHwBEGH9Hu+o3rFf9+ir5OzqUdNPNciwpVHQ2o0pmcCTHdFnw2D0WADBYKH0AECH8Ho8aNm9UfeVa+VqalTB5ijIKlipm5Cizo0W0oryx563pk9g9FgAwuCh9ABDmDK9XjdvelnNNuXwNDYqfeLUyCosUe9kYs6NBf9mshd07AQCBQukDgDDxjW3/Z16mq5oOy7W6TJ11tYobf7kcTz6t+MuvMDsqLsDusQCAQKL0AUAYOG/bf8NQxslPpX/9vU57GhUz+lKNuP8BxU+8RhaLxeyoAIA/O/tmnavJrXRG+RFAlD4ACAPFWw/J0+nT2LYTusX5obI99aqNTtGGy27XMy/cR9kDgCDDGZ0YSpQ+AAgDCTVHtcD1oUZ21KrenqjV2TO0P/FSGRYrhQ/opW9MkWbUBQHEGZ0YSpQ+AAhh7YcPyVmySvee3K8mW7wqM6fq4+Rx8lusktj2H+gtRl0w1DijE0OJ0gcAIch9/JjqSovVuucj2ZKS1Jq3WL87lap2v/Xc57DtP9B7jLpgqHFGJ4YSpQ8AgkRvppZ5ak7JWVai5vd2yRofL8fSZUq7bY6ssbG6n6lpQL8x6oKhxhmdGEqUPgAIAhebWtbprJOzvExNO7fLEh2t9EX5Sps7X7aEhHP3wbb/QP8x6oKh9vUzOtm9E4FG6QOAINDd1LLKtz7WmI82qPHtLbJYLEq9fa7SFyySPTnZpKRAeGLUBWY4+2ZdZmaSamubzY6DMEbpA4AgcOEIQ6yvQ1Pr9+n6xoNqtEopM2YqfdESRaWnm5QQCG9fH3VhijSAcEPpA4AgcHZqWbTfo5vq9+vGhgOKNjr1Rfo4zfnrJxWdlWV2xJDEFvzoC6ZIY6hxODuGCqUPQFgLlRf9RdNGad+bZbrR+bHi/B4dTLhE72RNUX7hVApfP7EFP4Bgxu8oDCVKH4CwFWxPqF0V0Jsvd6hx21ZlrVktR2OjjiWP0sbka+XNGh60BTVUsAU/gGDG7ygMJUofgLAVTE+oFxZQV2O7dv/3aqW27pOtuUFxl1+hjOXP6vLxl+v2IU0WvtiCH0Aw43cUhhKlD4CpAjn9MpieUM8VUMPQhJYjmuHaI0dnk87EZ2ry83+t+KsmymKxDHmucMYW/ACCGb+jMJSsZgcAELnOjn6dfdI7O/2yel/NoNx/d0+cZjyhOhs7NK71uB49XqGC09vks1i1KmeW/n3YfCVMvJrCFwBFeWMVbT//aY4t+AEEC35HYSgx0gcEWKhsJGKGQE+/DIZztwzDUNuB/Xrk1Dplt52RKypJZdkzdSDxUsli4R3dAGILfgDBjMPZMZQofUAABdtGIsEm0NMvzX7R3/7F56orWaX2Tw8qIylF63Om6aOEMfJb/vTOLu/oBh5b8AMIZhzOjqFC6QMCKJg2EglGQ7GewYwX/R3HjspZskqtH++VLTlZmXffp5S8WWr4zKmjjDoBAIAhRukDAiiYNhIJRsEw/XIwuU+elLOsWC3v75Y1PkEZy+5U6q23yxrzpxLLqBMAADADpQ8IIHbm6pnZ0y8HS2dtrZzlpWp6Z6cs0TFKzy9Q2px5ssXHmx0NkMTaYgCIdJQ+IIDCbSQrEEJ59Kuzvl6uinI1bn9bFqtVaXPmKW3BQtmTks2OBpzD2mIAAKUPCKBwGcnC+bzNTapfu0YNWzbJ8PuVMjNPjsX5sqemmR0N+AbWFgMAKH1AgIXySBbO52trVf36KtVv2CDD41Zy7nQ58gsUlZn5p+lz/7WDcn8BphWaj7XFQPA6+zuSIxsQaJQ+ALgIv9utho0b5KqqlL+tVYk33KiMgqWKHjZcEtPnutPV9+U3q/frN6v3UwCHEGuLgeDEcweGEqUPALrh7/SocesWudZUyNfcpIRrJ8lRWKTYS0af93lMn+taV9+Xs3hxM3RYWwwEJ547MJQofQCC3lBPETS8XjXu3C5XRbm8LpfirpygjKXPKW7suC4/n+lzXbvYv38wXtwwffTign1tMT9DRCqeOzCUAlr6fvnLX6qyslKSlJeXp+9973vauXOnXnrpJbndbi1YsEDPP/98ICMACHFDOf3F8PvV/N67cpaVqvPMacWOGaOcRx5X/ISrevw6ps91rbvvy9cN5MUNU6N6L1jXFvMzRCTjuQNDyRqoO965c6e2b9+ukpISlZaWat++faqoqNALL7ygV199VWvXrtUnn3yirVu3BioCgDDQ0/SXwWIYhlo+fF9H//6HqvnN/0+W6GgN//Z3NOpvf3DRwif9afpctP38X6dMn+v6+3Khgby4Gci1Ub2vRv/r1R16dOUm/a9Xd6h6X02/c6D/huL/NxCseO7AUArYSF9mZqa+//3vKzo6WpI0duxYHTlyRKNHj9aoUaMkSfn5+aqqqlJeXl6gYgAIcYGc/mIYhtr2faKTFaVq+eKQorJzNOzJp5V4w42yWHv/nliwT58zy4XflwsN9MVNT9fGoys3dftzMGt0iWmM38T0NkSyr/+OZPdOBFrASt/48ePP/fnIkSOqrKzU/fffr8zMzHO3Z2Vl6fTp0326X4cjcdAyIrxlZiaZHSGibXn/uF6vPKC6+nZlpMXpwQUTNOv6UX2+n8y0ONXWt3d5+0B+xo379uvYf/6XmvYfUExWpsateFZZs/Nksdn6dX9LZiVpyazxF//ECPP178tgXRNndXdtnOVscuv1qk+VnBR73uOUbq/ucnSpdPuXvfoZ9ue62/L+cb1e9ancnb4es0WaQP3/Dibh8u9AYPDcgaES8I1cPv/8cz311FP63ve+J5vNpiNHjpz7O8MwZLFY+nR/TmeL/H5jkFMi3GRmJqm2ttnsGBHrwpGU2vp2/cvvP1JTc0ef38EsnHFZlzsPFs64rF8/444jR1RX8ke17ftEtpQUZd17v8YWLZazoUN1rrY+31+gvLHuoLZ+dFJ+Q7JapLzrhuuBeVeaHWtAJl6Sqp89lXvebQP5f9rVtXEhd6dP/1GxTxMvSf3LY3ZTFGvr2y+ap7+/W/6jYt+5wtdTtkhTOOMy/XvFfvm+9rRus6jf/7+DDc9F6C2uFfSW1Wrp1yBYQEvf+++/r+eee04vvPCCFi1apF27dqm2tvbc39fW1iorKyuQEQCYYDC3oR6sqZPuEyfkLCtWywfvy5qQoIw77lLq7NtkjYmRNSpKUkef7u9Cgzl17411B7X5w5PnPvYbOvdxqBe/wXSx6aNnXfh3ZmyewDTG7lmsFn299VmsfXszGABwcQErfadOndKzzz6rl19+Wbm5f3pnd9KkSfryyy919OhRjRw5UhUVFVq2bFmgIgAwyWC/wB3IzoOe06flXF2q5nffkTUmRo4lhUqdM0+2uLh+3V9XBnuN2NaPTnZ7O6XvfF+/Nv7Xqzt6VebMOLeOXfq6Vrz1kLy+82fveH0G55QBwCALWOn77W9/K7fbrZUrV5677e6779bKlSu1YsUKud1u5eXlaf78+YGKAMAkwfACt9PlkquiXI3b35bFblfavAVKn79QtsTBXxc82AfsdjeDnZntPettmTNj4x0OSO8aI6AAMDQCVvpefPFFvfjii13+XXl5eaAeFkAQMPMFrrepSa61FWrcskmGYSh11mylL8yXPTW11/fR16mag/3C1WrpuuAx661nfSlzQ31uHTu8di0Y3iACgEgQ8I1cAEQeM17g+lpbVb+uUvUbN8jo7FTytOly5BcoypHRp/vpz1TNwX7hmnfd8PPW9H399sEUjkcIBOsh5FJwZzMLI6AAMDQofQACYqhe4Po72lX/1gbVr6uUv71dSTfdLMeSpYrO6d9j92eq5mC/cD27bi+Qu3eadVZdKAjHMhysGAEFgKFB6QMQkvydHjVu3ixXZYV8zc1KuG6yMgqKFDNqYGee9Weq5oUvXBPj7DIMQ79ZvV/FWw/160XsA/OuDOimLYO9DjFcUIaHHiOgABB4lD4AIcXwetW4Y5tcFeXy1tcrfsJEOZYWKW7M4EwH6+9UzbMvXANdGgZrFIoNNLpGGQYAhCNKH9BPTAEbWobfr+Z3q+UsL1Vnba1ix45TzmNPKv7KCYP6OAOdqhnI0jCYhZINNLpGGQYAhCNKH9APTAEbOoZhqOWD3XKWlchz8qRiLhmt4c89r4RrrpXFMvjbWQ50jVEgS8NgFko20OgaZRgAEI4ofUA/MAUs8AzDUNsnH6uuZJXcx44qethwDVv+rBKnXC+L1RrQxx7IGqNAlYbqfTWDWijZQKNrlGEAQDii9AH9wBSwwGr79KDqSlap44vPFZWRqZxHn1DS1Nxelz0zp94GojScHVnuTn8LJRtofBNlGAAQjih9QD8wBSww2g8flrN0ldr275MtNVVZ9z+olBm3yGLv/a8qs6feBqI0dDWyfBajUIOPMgwACDeUPqAfmAI2uNxfHVddabFaP/pQtsQkZd51t1Jm3SprdHSf76u7qbe/rdgvaeiK32A+Tk8jyA8tuPLcY7G5EAAA6AqlD+gHpoB9U38Kh+d0jZxlpWp+711ZY2PlKCxS2u1zZI2N63eO7gqS31DIbrbT08jy1wsfmwsBAICuUPqAfmIK2F/0tXB0Op1yri5T087tstjtSl+wSGlz58uWmDjgLN0VJCl0N9vpzcgymwsBAIDuUPoADFhvC4e3sUGuNRVqfHuLJCn11tuUvmCx7Ckpg5alq4L0daG42U5vRpbZXAgAAHSH0gdgwC5WOHwtLXJVrVXDprdkeL1KmTFT6YuXKCrdMehZzhah31bsl9/45t8P9mY7Q7WO7mIjy2wuBAAAukPpAzBg3RWOnASLnKvLVL++Sv6ODiXdNFWOJYWKzs4OaJ6z5SjQm+0E0zo6NhfqGpvbAABA6QMwCC4sHHa/Vzc2f6aZX+2Xc0+bEidfL0fhUsWMGDlkmQK52c7Xi8SFzFpHx+ZC3xRMpRwAADNR+gAM2NkX0KWbP9PIE59oRsMnSuhsU/zEq5WxdJliL73M5ISD58Ii0RWz1tGxudD52NwGAIA/ofQBGDDD59NVDZ8r+6tyeevqFDf+cjmWLlP85VeYlilQozw9HZR+VjCvo4uk6Y5sbgMAwJ9Q+gD0m+H3q+X93aorK1ZnTY1iRl+q7PsfUvzEq2WxWEzNFqhRnt4UhmvHDv4GNYMh0qY7srkNAAB/QukDwlQgR3QMw1Dr3j1ylq6S+/hxRQ8foWHPrFDi5Cmml72zehrlqd5X0+/vRU/nAJ6195CzX/cdaJE23ZHNbQAA+BNKHxCGAjmi03Zgv+pKi9Vx6AtFZWYp5/EnlXTTVFms1gHnHkw9lbPfrN6v/9rwqe6dc0Wfvx8XOwdQ+kvhDLaplJE23TESN7cJtmsOABAcKH1AGArEiE77oS9UV7JK7QcPyJ6WrqwHH1bKtBmy2IPz18jFyllrh+9cEV4yK6nX93thkeiKIzkmKKdSRuJ0x0ja3CYYrzkAQHAIrrfmAQyKwRzR6Th2VCdeeVnHX/qxPCdOKPPue3XpT1cq9ZZZQVv4pD+9yH1owZU9fs7ZItyf+/7HZ6brifyrFG0//9fo2emDPRVvsxTlje02L0JfMF5zAIDgELyv2AD022CM6HhqTqmutEQtu3fJGh+vjKI7lHrr7bLGxg5m1ID4+hQ3q0XyG91/7kCmNvY0ffA3q/cP+uMNVCROd4wkkTZ9FwDQe5Q+IAwNZAOLzrpaOcvL1FS9Q5boaKUvzlfa3PmyxScEMvKguXCKW0+FTxr41Mbupg8G61TKSJruGGmC9ZoDAJiP0geEof6M6Hgb6uVcs1qNb2+VxWJR2u1zlbZwkexJyUMVe1B0d46exSIZFxTAQE5tZOdIDDWuOQBAdyh9QJjq7YiOr7lZrqo1ati0UYbfr5QZtyh98RJFpaUNQcrB191UNsOQnsi/asimNuZOzNEXXzVo60cn5Tckq0Wafk3fRtnYiRF9wfRdAEB3KH1AiBjsAuBra1P9hnVq2LBOfrdbyVOnKX1JgaIzswYx9dDraYrbUE5trN5Xox0f15ybXuo3pB0f12jcyNReZXhj3UFt/vDkuY/ZiRG9wfRdAEBXKH1ACBjMrdj9brcaNr0lV9Va+VtblXj9DXIULFXM8BGDntsMwTLFbSDHZlTvqzmv8PX16wEAAL6O0geEgME4d8/f2anGt7fItWa1fE1NSrjmWjkKixQ7+tIAJDZPsExx62knxep9NT3m6WmLfXZiBAAAfUXpQ9gKp/VQA9mK3fD51LRzu5yry+V1ORV3+RXKeHqF4saPH+yYQWOwprgN5BrqbpqppIuO0vb0c2UnRgAA0FeUPoSlLe8fH7TpkMGgP1uxG36/mt/bJWd5iTpPn1bsZWOU/dAjir9qoiwWSyDjhoWBTqntaprpWRcbpe2pMJ6dphpOb2oAAIDAovQhLL1eeWDA0yGDSV/WqRmGodaPPlRdabE8J75S9IiRGv7t7yhh0nWUvT4Y6JTas5/Tn0PauyuMsycPV+7EnEFd4xnOKMYAAPwJpQ9hqa6+vcvbQ3U9VG/WqRmGobb9++QsLVbHl4cVlZ2tnCeXK+mGm2SxWs2KHrIGsibvrNyJOed+ZhfqaZT2Yj/vwVjjGQ56KnUUYwAA/oLSh7CUkRan2i6KXyivh+ppnVr755+rruSPav/sU9nTHcp+6BElT5shi802xCnDx0DW5H1df3cT7ennPZA1nuHiYqWOYgwAwF/w9j/C0oMLJijafv7lbca2/YHWcfSIvvr//n90/Gc/kafmlDLvvV+X/mSlUmbmUfgGqChv7DeuobPOlofeyJ2Yo4cWXHnuDQdHcoweWnDlgIpHd29ehPKbGn3VU6mTKMYAAHwdI30wVaDW3My6fpSamjvCdj2P++QJOctK1PL+blkTEpSx7C6l3nqbrDGR86I/0AayJq+r+xrMay9YziI008VKXX82PwIAIFxR+mCaQK+5GewX2sHAU3tGzvJSNb9TLUt0jNLzC5Q2Z55s8fFmRwtL/V2TF2jBchahmS5W6ijGAAD8BaUPpmHNTe911tfLVVGmxu3bZLFalTZ3ntLnL5ItKcnsaGEvWMtDOL6p0RcX+7lQjAEA+AtKH0zDmpuL8zY3ybV2jRo3b5RhGEq5ZZYcixbLnppmdrSIQXkITr35uUR6MQYA4CxKH0zDmpvu+dpaVb++SvUb1svweJScO12OJQWKysg0O1pECqfy0N062lA80y6cfi4AAAQSpQ+mCdZpc2byd3SoYdNbclWtlb+tTYk33KSMgkJFDxtudjSEge7W0X7xVYN2fFzDmXYAAIQpSh9Mw7S5v/B3etS4ZbNca9fI19ykhGsnyVFYpNhLRpsdDQMUTCNo3a2j3frRSfkNfeN21tcCABAeKH0wVaRPzzK8XjXu2C5XRbm89S7FT7hKjsIixY0dZ3Y0DIJA71DbV92tl72w8F3s8wEAQGih9AEmMPx+Ne96R86yUnXWnlHsmLHKefRxxU+4yuxoGETBtkNtd+torZauix/rawEACA+UPmAIGYahlg8/kLO0WJ6TJxQzapSGr/grJVw7SRaLxex4GGTBtkNtd+top1+Tc96avrO3R/L6WgAAwgmlDxgChmGobd/HqisplvvoEUXl5GjYU88o8fobZLFazY43ZIJpfdtQCLYdantaRztuZGpE/WwAAIgklD4gwNo++1TOklVq//wz2TMylP3IY0qeOk0Wm83saEMq2Na3DYVg3KG2u3W0kb6+FgCAcEbpAwKk48iXqitZpbZ9n8iWkqqs+x5Qysw8WeyR+d8u2Na3DQV2qAUAAMEgMl99AgHkPvGVnKUlavnwfVkTE5Vx57eUOutWWWMiZ1OMrqZxBtv6tqHCCBoAADAbpQ8YJJ7Tp+UsL1HzrndljY2Vo2CpUm+fK1tcnNnRhlR30zgT4+xqafd+4/PZIRIAACCwKH3AAHW6nHJVlKtx+zZZ7HalzVug9PkLZUtMNDvaoOrtJizdTeOMslsUbbcG1fo2AACASEDpA/rJ29goV2WFGrdsliSlzrpV6YsWy56Sam6wAOjLJizdTdds7fDpifyrWN8GAAAwxCh9QB/5WltVv65S9W+tl+H1KnnaDDnyCxTlcJgdLWD6sglLT8cUsL4NAABg6FH6gF7yd7Sr/q0Nql9XKX9Hh5JuvFmOgkJFZ4dWienPWXl92YQlGI8p6K9QP1cw1PMDAIDBQekDLsLv8ahxyya51q6Rr6VZCddNVkZhkWJGjjI7Wp/196y8vhwyHi7HFIT6uYKhnh8AAAweSh/QDcPrVeP2t+WsKJevoUHxV02Uo3CZ4saMMTtav/X3rLy+jt6FwzTOUD9XMNTzAwCAwUPpAy5g+P1qfqdazvJSddbVKnbceA17Yrnir7jS7Gjf0Nfpe/09Ky9cRu/6ItTPFQz1/AAAYPBQ+oA/M/x+tXzwvpxlJfKcOqmYS0ZrxHe+q/irr5HFYgnIYw5kzVV/pu/1ZZrmhcwevRvq9WkD+V4Fg1DPDwAABo/V7ACA2QzDUMvePTr247/XqV//SpI07OlndckP/rcSrrk2oIXvtcqD516Yny1t1ftqevX1PU3f605R3lhF28//bx8Km6wM9HvVH6H6vTor1PMDAIDBw0gfIlrbwQOqK1mljkNfKCozUzmPPaGkm3Nlsf7lxXKgRpgGuuaqP9P3QnWaphnr00L1e3VWqOcHAACDh9KHiNR++LCcJavUdmCf7GlpynrgIaVMnymL/fz/EoHcAXGga676O33P7Gma/WHW+rRQ/F59XajnBwAAg4PSh4jiPn5cdWXFav3oQ9mSkpR51z1KmTVb1ujoLj8/kCNMA11zFU7n4V1MINenVe+rUen2atXWtzMaBgAAwhKlDxHBU1MjZ3mJmt/bJWtsrByFRUq7fa6ssbE9fl0gR5gGWtoiafpeoAouZ9kBAIBIENDS19LSorvvvlu//vWvNXLkSO3cuVMvvfSS3G63FixYoOeffz6QDw+o01kn5+oyNe3cIUtUlNIXLFLavAWyJST06usDOcI0GKUtUqbvBargcpYdAACIBAErfXv27NGLL76oI0eOSJI6Ojr0wgsv6I033tCwYcP01FNPaevWrcrLywtUBEQwT329zvzXf6th6xZZLBal3nq70hculj05uU/3E+gplJFS2gZDIL5XnGUHAAAiQcBK3+9//3v96Ec/0ve+9z1J0t69ezV69GiNGjVKkpSfn6+qqipKHwaVr6VFrqq1+mLTW/L7fEqZPlPpi/MVle7o1/1F0hTKSMRZdgAAIBIErPT95Cc/Oe/jM2fOKDMz89zHWVlZOn36dJ/v1+FIHHA2hB9vW5tOllfoZNlq+drblZk3U6Pu/pbihg28nC2ZlaQls8YPQkoEm4cXT9Qv/7BH7k7fudtiomx6ePFEZWYmmZgMwYxrA33B9YLe4lpBIA3ZRi5+v/+8Q64Nw+jXoddOZ4v8fmMwoyGE+d1uNWzeKFfVWvlbWpQ45Xo5Coo08rorVVvbrJbaZrMjIoh0debig/OvUOn2L8/bvXPiJamq5dpBFzIzk7g20GtcL7iYs89Lria30plNhF6wWi39GgQbstKXk5Oj2tracx/X1tYqKytrqB4eYcbwetX49hY511TI19ig+KuvUUbhMsVeeqnZ0RCEqvfV6L/f+kwt7d5zt53dqfOhBVfq31+cywszAMCQYgdpDKUhK32TJk3Sl19+qaNHj2rkyJGqqKjQsmXLhurhESYMn09N1TvlXF0qr9OpuMuvkOOppxV/+RVmR0OQuvBJ9evO7tQZqOm7XY0s8kQOAJDYQRpDa8hKX0xMjFauXKkVK1bI7XYrLy9P8+fPH6qHR4gz/H617H5PdeUl6qypUcyllyn7wUcUf9XEfk0TRuTo6kn16wK1Uyfv4AIAesIO0hhKAS99mzZtOvfn3NxclZeXB/ohEUYMw1Drno/kLCuW+/hxRY8YqeHPrlDCdVMoe+iViz15BmqnTt7BBQD0hB2kMZSGbKQP6Ku2A/tVV7JKHYcPKSorWzlPPKWkG2+WxWo1OxpCSHdPqtLgnrl4Id7BBQD0JNBnAQNfR+lD0Gk/9IXqSlap/eAB2dPTlf3gI0qeNl0WO5cr+q6rJ1VJSoi16d45VwRs1I13cAEAPfn6WcDs3olA41U0gkbHsaNylharde8e2ZKSlXn3fUrJy5M1KtrsaAhhX39SHcoNVXgHFwBwMbkTc5Q7MYfjPRBwlD6YznPqpOrKStSy+z1Z4xOUUXSHUm+bI2sMIyIYHGefVIf6MaWhL5sAAAAXovTBNJ21tXKuLlVT9U5ZomOUvniJ0ubOky0+wexowKAwo2wCAABciNKHIedtqJezYrUat22VxWpV2px5SluwUPakZLOjAQHBeX0AAMBMlD4MGV9zs1yVa9SweaMMv18pM/OUvihfUWlpZkcDAobz+gAAgNkofQg4X1ub6tdXqX7Dehket5Jzp8mRX6iozEyzowEBF4rn9TEyCQBAeKH0IWD8brcaNm6Qq6pS/rZWJd5woxxLlipm+HCzowFDJtTO62NkEgCA8EPpw6Dzd3aqcesWudaulq+pSQnXTpKjsEixl4w2Oxow5ELtvL5QHJkEAAA9o/Rh0Bg+n5p2bJezokxel0txV1ypjGdWKG7ceLOjAaYJtfP6Qm1kEgAAXBylDwNm+P1qfu9dOctK1XnmtGLHjFHOI48r7soJslgsZscbcqyHwteF2nl9oTYyCQAALo7Sh34zDEOtH32gutISeU58peiRozT8299RwqTrIrLsSayHQtdC6by+UBuZBAAAF0fpQ58ZhqG2/ftUV7JK7iNfKio7R8OefFqJN9woi9VqdjxTsR4KoS7URiYBAMDFUfrQJ+2ff6a6klVq/+xT2dMdyn74USXnTpfFZjM7WlBgPRTCQSiNTAIAgIuj9KFXOo4cUV3pKrV98rFsKSnKuvd+Jc/MkzUqyuxoQYX1UAAAAAg2lD70yH3yhJylxWr54H1ZExKUccddSp19m6wxlJiusB4KAAAAwYbShy55zpyRc3Wpmt+pljUmRun5BUqbM0+2+HizowU11kMBAAAg2FD6cJ5Ol0uuinI17tgmi82mtLnzlb5gkWyJiWZHCxmshwIAAEAwofRBkuRtapJrbYUat2ySYRhKzZul9IX5sqemmh0NAAAAwABQ+iKcr7VV9esqVb9xgwyPR8nTZsiRv0RRGZlmRwMAAAAwCCh9Ecrf0aH6jRtUv65S/rY2Jd14kxwFSxWdM8zsaEDYqt5Xw3pPAAAw5Ch9Ecbf6VHj5s1yVVbI19yshEnXKaOwSDGjLjE7WljjxT6q99Wct7Ors8mt1yoPShLXAgAACChKX4QwvF417tguV0WZvPX1ip9wlRyFRYobO87saGGPF/uQ/rSj69eP8pAkj9ev4q2HuA4AAEBAUfrCnOH3q/ndd+QsL1Vn7RnFjh2nnMeeVPyVE8yOFjF4sd+zSBkFdTa5+3Q7AADAYKH0hSnDMNTywftylhXLc/KkYkZdouHP/ZUSrpkki8VidryIwov97kXSKKgjOabLn7kjOcaENAAAIJJQ+sKMYRhq++Rj1ZWskvvYUUXnDNOw5c8occoNslitZseLSLzY714kjYIW5Y09r+BKUrTdqqK8sSamAgAAkYDSF0baPvtUzpJVav/8M9kzMpT9yONKzp1G2TMZL/a7F0mjoGdLbCRMZQUAAMGF0hcGOr48rLrSYrXt+0S21FRl3fegUmbeIoudH28w4MV+9yJtFDR3Yg4/dwAAMORoBSHM/dVx1ZWVqPXDD2RLTFLGnd9S6uzbZI2ONjsaLsCL/a4xCgoAABB4lL4Q5Dl9Ws7yEjXvelfW2Fg5CpYqbc5cWWPjzI4G9AmjoAAAAIFH6QshnU6nnBVlatqxXRa7XWnzFyp93gLZEhPNjgb022CNgkbK0Q8AAAB9RekLAd7GRrnWVqhx62ZJUurs25S+cJHsKanmBgOCRCQd/QAAANBXlL4g5mtpkWtdpRo2bpDh9Sp5+gw5FhcoyuEwOxoQVCLp6AcAAIC+ovQFIX9Hu+o3rFf9+ir5OzqUdNPNciwpVHQ2L16BrkTS0Q8AAAB9RekLIn6PRw2bN6q+cq18Lc1KmDxFGQVLFTNylNnRgKB2saMfhmK9H2sKAQBAsKL0BQHD61XjtrflXFMuX0OD4iderYzCIsVeNsbsaIggoVxaejr6YSjW+7GmEAAABDNKn4kMv19N1TvlXF0qb12d4sZfLseTTyv+8ivMjoYIE+qlpaejH/7XqzsCvt6PNYUAACCYUfpMYPj9avlgt5ylJfLUnFLM6EuVff+Dip94jSwWi9nxEIHCobR0d/TDUKz3Y00hAAAIZpS+IWQYhlo/3iNnSbHcx48pevhwDXv620qccj1lD6YK59JysfV+ofIYAAAA/UXpGyJtBw+ormSVOg59oajMTOU89qSSbp4qi9VqdjQgbEtL9b4auTt937j97Hq/wdLTmkIAAACzUfoCrP3wITlLVqntwH7Z09KU9cDDSpk+QxY733oEj3AsLReuUzwrIdame+dcMajTVntaUwgAAGA2mkeAuI8fU11psVr3fCRbUpIyv3WPUmbNljUq2uxowDeEY2npap2iJMVG2wPy7+puTSEAAIDZKH2DzFNzSs6yEjW/t0vW+Hg5li5T2m1zZI2NNTsa0KNwKy3hvE4RAACgLyh9g6TTWSdneZmadm6XJTpa6YvylTZvvmzxCWZHAwIi2M/1C9d1igAAAH1F6Rsgb0ODnGtWq/HtLbJYLEq9fa7SFyySPTnZ7GhAwITCuX7huE4RAACgPyh9/eRraZGrco0aNm+U4fMpZcZMpS9aoqj0dLOjAQEXCuf6heM6RQAYKsE+mwNA31D6+sjX3q769VVq2LBOfrdbSVNz5cgvVHRWltnRIg5PSOYJlfVy4bZOEQCGQijM5gDQN5S+XvK73WrYtFGuqjXyt7Yq8fob5ChYqpjhI8yOFpF4QjIX6+UAIHyFwmwOAH1D6bsIf2enGrdtlWvNavkaGxV/9bXKKCxS7KWXmh0tovGEZK5gXy/HKDAA9F+ozOYA0HuUvm4YPp+aqnfIWV4mr8upuMuvUMbyZxU3/nKzo0E8IZktmNfLMQoMAAPDbA4g/FD6LmD4/WrevUvOslJ1nq5RzKWXKfuhRxR/1URZLBaz4+HPeEIyX7Cul2MUGAAGJthncwDoO0rfnxmGodY9H6mutFier44resRIDX/2OSVcN5myF4R4QkJ3GAWGmZhajHAQzLM5APRPxJc+wzDUdmC/nKWr1HH4sKKys5XzxHIl3XiTLFar2fHQDZ6Q0B1GgWEWphYjnATrbA4A/RPRpa/9i89VV7JK7Z8elD09XdkPPaLkaTNksdnMjoZe4AkJXWEUGGZhajEAIFhFZOnrOHZUzpJVav14r2zJycq85z6l3DJL1qgos6MBGCBGgWEWphYDAIJVRJU+98mTcpYVq+X93bLGJyhj2Z1KvfV2WWOY9gWEE0aBYQamFgMAglVElD5P7Rm5ysvU9M5OWaJjlJ5foLQ582SLjzc7GgAgTITa1GI2nQGAyBHWpa+zvl6uinI1bn9bFqtVaXPnKX3+ItmSksyOBgAIM6E0tZhNZwAgsoRl6fM2N6l+7Ro1bNkkw+9Xysw8ORbny56aZnY0AEAYC5WpxWw6AwCRJaxKn6+tVfXrq1S/YYMMj1vJudPlyC9QVGam2dEAAAgabDoDAJElLEqf3+1Ww8YNclVVyt/WqsQbblJGQaGihw03OxoAAEGHTWcAILKEdOnzd3rUuHWLXGsq5GtuUsK1k+QoLFLsJaPNjgYAQNAKtU1nAAADY0rpW716tf71X/9VXq9XDz30kO67774+fb3h9apx53a5KsrldbkUd+UEZSx9TnFjxwUoMQAA4SOUNp0BAAzckJe+06dP6+WXX1ZxcbGio6N199136+abb9a4cb0rbC0fvq/Tb76pzjOnFTtmrHIeeVzxE64KcGoAAPoumI9FCJVNZwAAAzfkpW/nzp2aOnWqUlNTJUnz5s1TVVWVvv3tb/fq6xvfWq+YnBwNe+hhxV05QRaLJYBpEcqsVq4N9B7XC3qrt9fKB5/Xau07R2WzWZWVFidJWvvOUcXF2jVlPBuMRQp+t6C3uFbQG/29Toa89J05c0aZX9tNMysrS3v37u3111+78ieBiIUw5HAkmh0BIYTrBb3V22tljiNRc6ZeFuA0CHb8bkFvca0gkKxD/YB+v/+80TnDMBitAwAAAIAAGfLSl5OTo9ra2nMf19bWKisra6hjAAAAAEBEGPLSN23aNFVXV8vlcqm9vV3r16/XLbfcMtQxAAAAACAiDPmavuzsbD3//PN68MEH1dnZqTvuuEPXXnvtUMcAAAAAgIhgMQzDMDsEAAAAACAwhnx6JwAAAABg6FD6AAAAACCMUfoAAAAAIIxR+gAAAAAgjFH6AAAAACCMhUTpW716tRYuXKi5c+fq//2//2d2HASplpYWLV68WF999ZUkaefOncrPz9fcuXP18ssvm5wOweKXv/ylFi1apEWLFunnP/+5JK4VdO8Xv/iFFi5cqEWLFul3v/udJK4X9OxnP/uZvv/970viWkH3HnjgAS1atEgFBQUqKCjQnj17uF7QpU2bNqmoqEgLFizQj3/8Y0n9/N1iBLmamhpj9uzZRn19vdHa2mrk5+cbn3/+udmxEGQ++ugjY/HixcbEiRON48ePG+3t7UZeXp5x7Ngxo7Oz03j00UeNLVu2mB0TJtuxY4fxrW99y3C73YbH4zEefPBBY/Xq1Vwr6NK7775r3H333UZnZ6fR3t5uzJ492zhw4ADXC7q1c+dO4+abbzb+5m/+huchdMvv9xszZswwOjs7z93G9YKuHDt2zJgxY4Zx6tQpw+PxGPfcc4+xZcuWfl0rQT/St3PnTk2dOlWpqamKj4/XvHnzVFVVZXYsBJnf//73+tGPfqSsrCxJ0t69ezV69GiNGjVKdrtd+fn5XDdQZmamvv/97ys6OlpRUVEaO3asjhw5wrWCLt100016/fXXZbfb5XQ65fP51NTUxPWCLjU0NOjll1/W8uXLJfE8hO4dPnxYkvToo49qyZIl+s///E+uF3Rpw4YNWrhwoXJychQVFaWXX35ZcXFx/bpWgr70nTlzRpmZmec+zsrK0unTp01MhGD0k5/8RDfccMO5j7lu0JXx48fruuuukyQdOXJElZWVslgsXCvoVlRUlF555RUtWrRIubm5/G5Bt374wx/q+eefV3JysiSeh9C9pqYm5ebm6le/+pX+4z/+Q//zP/+jkydPcr3gG44ePSqfz6fly5eroKBA//Vf/9Xv3y1BX/r8fr8sFsu5jw3DOO9joCtcN+jJ559/rkcffVTf+973NGrUKK4V9Oi5555TdXW1Tp06pSNHjnC94Bv+8Ic/aNiwYcrNzT13G89D6M7kyZP185//XElJSUpPT9cdd9yhV155hesF3+Dz+VRdXa2f/vSnevPNN7V3714dP368X9eKPZBBB0NOTo5279597uPa2tpzU/iA7uTk5Ki2tvbcx1w3OOv999/Xc889pxdeeEGLFi3Srl27uFbQpUOHDsnj8WjChAmKi4vT3LlzVVVVJZvNdu5zuF4gSWvXrlVtba0KCgrU2NiotrY2nThxgmsFXdq9e7c6OzvPvUlgGIZGjBjBcxG+ISMjQ7m5uUpPT5ck3X777f1+Hgr6kb5p06apurpaLpdL7e3tWr9+vW655RazYyHITZo0SV9++eW5YfGKigquG+jUqVN69tln9U//9E9atGiRJK4VdO+rr77Siy++KI/HI4/Ho40bN+ruu+/mesE3/O53v1NFRYXKysr03HPP6dZbb9W//du/ca2gS83Nzfr5z38ut9utlpYWlZSU6Lvf/S7XC75h9uzZ2r59u5qamuTz+bRt2zbNnz+/X9dK0I/0ZWdn6/nnn9eDDz6ozs5O3XHHHbr22mvNjoUgFxMTo5UrV2rFihVyu93Ky8vT/PnzzY4Fk/32t7+V2+3WypUrz9129913c62gS3l5edq7d68KCwtls9k0d+5cLVq0SOnp6VwvuCieh9Cd2bNna8+ePSosLJTf79e9996ryZMnc73gGyZNmqTHH39c9957rzo7OzV9+nTdc889GjNmTJ+vFYthGMYQZAYAAAAAmCDop3cCAAAAAPqP0gcAAAAAYYzSBwAAAABhjNIHAAAAAGGM0gcAAAAAYSzoj2wAACCQrrjiCl1++eWyWq2yWCxqb29XYmKi/vf//t+65ppr+nWfX331lfLz8/Xhhx8OcloAAPqO0gcAiHivvfaa0tPTz33829/+Vj/+8Y/15ptvmpgKAIDBQekDAOBrvF6vTp06pZSUlHO3/eu//qvWr18vv9+vESNG6Ec/+pGys7P10Ucf6R//8R/l8XhUW1uradOm6ac//amJ6QEA+CZKHwAg4j300EOSpPr6esXExGj27Nl66aWXJEmlpaX67LPP9Ic//EF2u11vvvmmXnzxRf3mN7/R66+/rueee04333yzWltbddttt+mTTz5Ramqqif8aAADOR+kDAES8s9M79+3bpyeffFI333yzHA6HJGnz5s36+OOPtWzZMkmS3+9Xe3u7JGnlypV6++239etf/1qHDx+W2+1WW1sbpQ8AEFQofQAA/NnEiRP1t3/7t/r+97+vCRMmaOTIkfL7/Xr88cd17733SpI8Ho8aGxslSffff7+uuOIKzZw5UwsWLNCePXtkGIaZ/wQAAL6BIxsAAPiaxYsX69prrz03vXPGjBn64x//qJaWFknSL37xC33ve99TU1OTPv74Y/31X/+15s6dq5qaGh07dkx+v9/M+AAAfAMjfQAAXOAHP/iBlixZom3btunOO+/U6dOnddddd8lisWjYsGFauXKlkpOT9eSTT2rp0qWKj49Xdna2pkyZoqNHj2rUqFFm/xMAADjHYjAPBQAAAADCFtM7AQAAACCMUfoAAAAAIIxR+gAAAAAgjFH6AAAAACCMUfoAAAAAIIxR+gAAAAAgjFH6AAAAACCM/f8B0kANdkuyv+QAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Y_predict=SGDReg.predict(test_data_poly)\n", + "\n", + "\n", + "fig = plt.figure()\n", + "plt.scatter(Y_test,Y_predict)\n", + "plt.xlabel('Real')\n", + "plt.ylabel('Predicted')\n", + "plt.xlim(0,60)\n", + "plt.ylim(0,60)\n", + "plt.plot([0,60],[0,60],'r')\n", + "#SORUM OLACAKTI SON KULLANIMLA İLGİLİ\n", + "#pol derecem 2 olduğu halde neden yine lineer bir yaklaşım çizdi grafik tam olarak anlayamadım" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tpdb1cccJwpw" + }, + "source": [ + "# Feedback\n", + "If there are any part which is not clear to you, you can write down your questions here.\n" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "GradientDescent_V2.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/task2-logistic-regression/LogisticRegression.ipynb b/task2-logistic-regression/LogisticRegression.ipynb index 5e1c0e1..a914f69 100644 --- a/task2-logistic-regression/LogisticRegression.ipynb +++ b/task2-logistic-regression/LogisticRegression.ipynb @@ -51,7 +51,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -106,7 +106,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -173,7 +173,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -198,7 +198,7 @@ "\n", "# If we used only predict method, we would get 1 for values over 0.5 and 0 for the below. We used proba to see\n", "# probability distribution.\n", - "prediction = regressor.predict_proba(X.reshape(-1, 1))[:,1]\n", + "prediction = regressor.predict_proba(X.reshape(-1, 1))[:,1:3]\n", "plt.plot(X_train,y_train,'*', label='Train Samples')\n", "plt.plot(X_test,y_test,'*', label='Test Samples')\n", "plt.plot(X,Y, label='Base Model')\n", @@ -206,7 +206,7 @@ "plt.legend()\n", "plt.xlabel('Height')\n", "plt.ylabel('Probability')\n", - "plt.show()" + "plt.show()\n" ] }, { @@ -218,7 +218,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test Accuracy: 0.85\n" + "Test Accuracy: 0.825\n" ] } ], @@ -242,158 +242,31 @@ "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n", - "0 63 1 3 145 233 1 0 150 0 2.3 0 \n", - "1 37 1 2 130 250 0 1 187 0 3.5 0 \n", - "2 41 0 1 130 204 0 0 172 0 1.4 2 \n", - "3 56 1 1 120 236 0 1 178 0 0.8 2 \n", - "4 57 0 0 120 354 0 1 163 1 0.6 2 \n", - "\n", - " ca thal target \n", - "0 0 1 1 \n", - "1 0 2 1 \n", - "2 0 2 1 \n", - "3 0 2 1 \n", - "4 0 2 1 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n", + " 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n", + " dtype='object')\n" + ] } ], "source": [ "import pandas as pd\n", + "from sklearn.linear_model import SGDRegressor #gradient descent regression\n", + "from sklearn.datasets import load_boston # taking the data\n", + "from sklearn.datasets import make_regression # \n", + "from sklearn.metrics import mean_squared_error\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.preprocessing import scale\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", "\n", "PatientData = pd.read_csv('heart.csv')\n", - "PatientData.head()" + "PatientData.head()\n", + "print(PatientData.columns)" ] }, { @@ -406,10 +279,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "X = np.asarray(PatientData[['age', 'sex', 'cp','trestbps','chol','fbs','restecg','thalach', 'exang','oldpeak','slope','ca','thal']])\n", + "y = np.asarray(PatientData['target'])\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] }, { "cell_type": "markdown", @@ -421,10 +305,160 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#to detect highly correlated features I created a heatmap\n", + "sns.set(rc = {'figure.figsize':(15,8)})\n", + "sns.heatmap(data=PatientData.corr(), annot=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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uZCgp2RDW9nl5buXm5oe1LaLH19Wuzxv3RbWmv69LkpSQOCxqNX1d7dJQ5oSLVyFfs/ja174mSZo8ebImT55sYUuxg2ncY192do4tdc9/EBmXPSaKVceopeWEFOiNYk0MFJxLMsjNzefTPf6jhQsX2VL3/Gh15cp1Ua/bdeJoVGvGmlU/eUifnmyP+H7HpKVq88ZHjevt3r1bTz31lPr6+nTnnXeGPFcUYQEAUfTpyXZ9fum3LNjvAeM6LS0tKi0tVVlZmYYMGaIFCxboW9/6lq6++mrjtpaGxR133KG2trbgxfBHH31UZ86c0aZNm9Td3a1p06apuLjYyhYAAP9fbW2tbrzxRqWmpko696BeZWWlfvjDHxq3tSwsAoGAGhoa9MYbbwTDoqurS4WFhdq+fbuysrK0ZMkSVVVVBSe9AgBYp7W19YJJYTMyMlRfXx/StpaFxT//+U9J514Z2N7ervnz5+uaa65RTk6OsrOzJUkej0eVlZWEBQBEgd/vl8PhCC4HAoELlr+IZWFx6tQpuVwurVu3Tr29vVq0aJHuvvvuf0u1lpaWfu03LW1EpFuNSUlJTnXZ3UQUJSU5lZ4+0u42oiYp6dy01NE+Zv6uBrfMzEwdOnQouOz1epWRkRHStpaFxfXXX6/rr78+uDx37lxt3bpV3/jGN4Jf60+qnXfyZKf8/oB5xTjX2+uzu4Wo6u31yes9bXcbUXP+9xvtY+bvqn8GWtBMmjRJv/jFL9TW1qbk5GTt2bNHGzaE9hyYZWFx6NAh9fb2yuVySToXDGPHjpXX6w2u059UAwBcnMsuu0zFxcVatGiRent7NXfuXE2cODGkbS0Li9OnT2vr1q3asWOHent7tWvXLj3yyCNasWKFGhsbdfnll6uiooIJCQHElTFpqSHd5hrOfkPh8Xjk8Xj6vX/LwuKmm27S4cOHNXPmTPn9fi1cuFDXX3+9Nm/erGXLlqm7u1tut1uFhYVWtQAAA04oD84NRJY+Z7FixQqtWLHigq+5XC6Vl5dbWRYAEGHMMw4AMCIsAABGhAUAwIiwAAAYERYAACOmKAeAKHp07Wp1tH0a8f2mjB6jh366KaR1Ozs7tWDBAj399NO6/PLLQ9qGsACAKOpo+1QLx0d+yqIXG0ILoMOHD2vt2rVqaGjo1/45DQUAceTll1/W+vXr+z3VEiMLAIgjGzduDGs7RhYAACPCAgBgRFgAAIy4ZgEAUZQyekzIdy71d79WIiwAIIpCfRbCavv37+/X+pyGAgAYERYAACNOQ8WwE5192lbfFrV6nT1+SdKIIdH9DHKis0/jo1oRiD+ERYzKzs6Jes3WpkZJ0pjM6NYeL3uOF4gnhEWMWrhwUdRrlpRskCStXLku6rUBWItrFgAAI8ICAGBEWAAAjAgLAIARYQEAMCIsAABGhAUAwIiwAAAY8VAegH6J9jQykj1TyTCNzIUICwAhs2taFTumkhkvppH5V4QFgJDZMY2MxFQyAwHXLCzU3v6ZNm9+VB0d7Xa3AgAXhbCw0O7du3TkyAcqLy+zuxUAuCiEhUXa2z9TdXWVAoGAqqvfYnQBYFAjLCyye/cu+f0BSZLf72d0AWBQIywsUldXI5+vT5Lk8/Wprq7G5o4AIHyEhUVcrlw5neduNnM6E+Vy5drcEQCEj7CwiMczSwkJDklSQkKCpk+fbXNHABA+wsIiqamjlJfnlsPhUF5evlJSUu1uCQDCxkN5FvJ4Zumjj44zqgAw6BEWFkpNHaVVqx6yuw0AuGichgIAGBEWAAAjwgIAYERYAACMCAsAgBFhAQAwIiwAAEaWh0VJSYlWrVolSaqtrZXH41FBQYFKS0utLg0AiBBLw6Kurk67du2SJHV1dWnNmjV68skn9dprr+mvf/2rqqqqrCwPAIgQy8Kivb1dpaWlWrp0qSSpvr5eOTk5ys7OVmJiojwejyorK60qDwCIIMvC4qGHHlJxcbEuvfRSSVJra6vS09OD38/IyFBLS4tV5QEAEWTJ3FA7d+5UVlaWXC6XysrOvSHO7/fL4XAE1wkEAhcshyotbUTE+kRkJSU5JUnp6SNt7iT2xdvPOt6OdyCyJCxee+01eb1ezZgxQx0dHfr888/10Ucfyel0Btfxer3KyMjo975PnuwMvq4UA0tvr0+S5PWetrmT2BdvP+vBeryxFG6WhMW2bduC/y4rK9PBgwf1yCOPqKCgQI2Njbr88stVUVGhOXPmWFEeABBhUZuifOjQodq8ebOWLVum7u5uud1uFRYWRqs8AOAiWB4Ws2fP1uzZ517+43K5VF5ebnVJAECE8QQ3AMCIsAAAGBEWAAAjwgIAYERYAACMonbrLIAL1dS8perq8CbTbGpqlCSVlGzo97Z5eW7l5uaHVRfxi7AABqGUlBS7W0CcISwAm+Tm5vMJH4MG1ywAAEaEBQDAiLAAABgRFgAAI8ICAGBEWAAAjAgLAIARYQEAMCIsAABGhAUAwIiwAAAYERbAINTU1KD7779Lzc2NdreCOEFYAIPQs8/+SmfPntUzz/zS7lYQJwgLYJBpamrQxx9/JEn6+OOPGF0gKggLYJB59tlfXbDM6ALRQFgAg8z5UcV/WgasQFgAg8yXvjT2C5cBKxAWwCBz7733X7C8ZMkPbeoE8YSwAAaZcePGB0cTX/rSWGVn59jcEeIBYQEMQvfee7+Sk5MZVSBqEu1uAANPTc1bqq6u6vd2TU3nbuEsKdkQVt28PLdyc/PD2jbejBs3Xr/61XN2t4E4QlggYlJSUuxuAYBFCAv8m9zcfD7hA7gA1ywAAEaEBQDAiLAAABgRFgAAI8ICAGBEWAAAjAgLAIARYQEAMCIsAABGhAUAwIiwAAAYERYAACPCAgBgRFgAAIwICwCAEWEBADAiLAAARpaGxc9//nMVFRXplltu0bZt2yRJtbW18ng8KigoUGlpqZXlAQARYtlrVQ8ePKh33nlH5eXl6uvrU1FRkVwul9asWaPt27crKytLS5YsUVVVldxut1VtAAAiwLKRxTe/+U09//zzSkxM1MmTJ+Xz+XTq1Cnl5OQoOztbiYmJ8ng8qqystKoFAECEWHoaKikpSVu3btUtt9wil8ul1tZWpaenB7+fkZGhlpYWK1sAAESAZaehznvggQd0zz33aOnSpWpoaJDD4Qh+LxAIXLAcirS0EZFuEcAAl5TklCSlp4+0uZP4ZVlYfPjhh+rp6dG1116r5ORkFRQUqLKyUk6nM7iO1+tVRkZGv/Z78mSn/P5ApNsFMID19vokSV7vaZs76Z9YCjfLTkMdP35ca9euVU9Pj3p6erRv3z4tWLBAx44dU2Njo3w+nyoqKpSfn29VCwCACLFsZOF2u1VfX6+ZM2fK6XSqoKBAt9xyi0aPHq1ly5apu7tbbrdbhYWFVrUAAIgQRyAQGFTndDgNBcSfkpINkqSVK9fZ3En/xNJpKMICQFTU1Lyl6uqqsLZtamqUJI0blxPW9nl5buXmRv+UdyyFheV3QwHAxUpJSbG7hbjHyAIALBJLIwsmEgQAGBEWAAAjwgIAYERYAACMCAsAgBFhAQAwIiwAAEaEBQDAiLAAABgRFgAAo0E3N1RCQv/erAcAuHiDbm4oAED0cRoKAGBEWAAAjAgLAIARYQEAMCIsAABGhAUAwIiwAAAYERYAACPCAgBgRFjEoN27d6uoqEgFBQV64YUX7G4HMaSzs1O33nqrjh8/bncriDLCIsa0tLSotLRUL774ov74xz/qpZde0tGjR+1uCzHg8OHDuu2229TQ0GB3K7ABYRFjamtrdeONNyo1NVXDhw/X1KlTVVlZaXdbiAEvv/yy1q9fr4yMDLtbgQ0G3ayz+GKtra1KT08PLmdkZKi+vt7GjhArNm7caHcLsBEjixjj9/vlcPzvNO6BQOCCZQAIB2ERYzIzM+X1eoPLXq+X0wYALhphEWMmTZqkuro6tbW16ezZs9qzZ4/y8/PtbgvAIMc1ixhz2WWXqbi4WIsWLVJvb6/mzp2riRMn2t0WgEGON+UBAIw4DQUAMCIsAABGhAUAwIiwAAAYERYAACPCAgBgRFgAAIwIC8S0M2fO6IEHHtCMGTM0a9YsrV27Vn6/X/v379e8efM0c+ZMLViwQO+9954kafXq1Vq+fLkk6ciRI3K5XPrwww/tPARgQOAJbsS0vXv36syZM3r11Vfl8/m0fv16NTU1qbS0VM8//7xGjRqlI0eOaPHixdqzZ4/WrVunWbNmadeuXXruuee0evVqXXXVVXYfBmA7nuBGTGtubtb3vvc9jRs3TpMmTdKUKVN08OBBbd26VZmZmcH12tra9Otf/1pf/vKX9fe//13z58/X9OnT9dhjj9nYPTBwMLJATMvOztbevXt14MABvfPOO1q8eLGWLFkil8uln/3sZ8H1Pvnkk+DsvMeOHVNqaqref/999fT0aMiQITZ1DwwcXLNATHvxxRe1evVq5eXl6cc//rHy8vLU0dGhmpqa4LWIqqoqTZ8+XV1dXTp+/Lg2btyo3/zmN7ryyiu1ZcsWm48AGBgYWSCmzZw5UwcPHlRRUZGSk5OVlZWlO+64Q1dddZUefPBBBQIBJSYm6qmnntLQoUP1ox/9SHfddZeuueYaPfTQQ/J4PJo0aZImT55s96EAtuKaBQDAiNNQAAAjwgIAYERYAACMCAsAgBFhAQAwIiwAAEaEBQDAiLAAABj9P+LWDRzsbZoMAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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0aJDy8/M1e/bsXk9cVFR02Wvr16/XvHnzelyGOnXqlGw2m3fbZrOpubm5T8X3hRmrknncXUpOHhYx47oXLMj1+1jGoQPRr9eg6OrqktPpVFJSkiTp3LlzPR6866uamhqdPHlSTz/9tA4cOOB93e1293g+w+Px+PW8xtChSVd83Wo1ZzorqzVGNtsgn4+Li7NKkl/HmsGMes16jy61G2pxcdaI+SzxHkWnXoNizpw5+vu//3vNnDlTFotFb7/9tnJycnxuqKKiQkePHtV9992n8+fPq6WlRY8//riWLl0qh8Ph3a+lpUV2u93n858+7ZTbfXmAJSVdL2tiR8hXJUtKul4Ox5c+H9vZ6ZIkv441gxn1mvUeXWo31Do7XRHzWeI9+lo0hU+vQbFw4ULdfPPNqqurU2xsrJ588klNmzbN54bWrl3r/fOBAwf08ssv69/+7d8kSQkJCaqvr9edd96psrIyZWZm+nx+AOGBgRHRp09jUL/zne/oO9/p/kbu8XjU0NCg8ePHB6yI4uJiFRYWyul0auLEicrN9f+aOQDzMDAiOvUaFK+//rpeeOEFXbhwwfvakCFDVFNT06cGqqqqLnttypQpmjJlinc7NTVVpaWlfTofgPDFwAjfPPzwwyouLtaQIUOC2s6vfvUrdXR06Hvf+55fx/d6p/dnP/uZtmzZomnTpmnXrl1avHixvvvd7/rVGADga339wn2t6uvr1d7e7vfxvfYoUlJSNGnSJN166606ffq0fvzjH2vWrFl+NwgA6H5GTZK+//3v64c//KHeeOMNdXR06MyZM5ozZ44ef/xxHThwQEVFRRo4cKDOnTunHTt26NVXX1Vpaamuu+46TZ48WXv37lVVVZU6OjpUXFys//3f/5XL5dJtt92mwsJC1dXVqaqqSjU1NUpMTPSrV9FrjyI2NlZtbW0aN26cDh06JElyucwZ2QAA0eLSAJ///u//1o4dO7Ru3Trt3LlT27dv16ZNm3TmTPfMDkePHtX69etVXl6uAwcOaOfOnSotLdXOnTt17tw57/k2bdokq9WqnTt3avfu3bLb7SouLtbdd9+tb3/728rLy/P70lOvPYoHHnhACxcu1MaNGzVnzhy9/fbbuvHGG/1qDABwuY0bN2rfvn2qqKjQJ598Io/H470vPGLECI0aNUqStH//fs2cOVPXX3+9JOl73/uefv/730uS9u3bpy+//FK1tbWSupeIGDp0aEDq6zUo7r//fs2aNUsDBw7U9u3b9cc//lF/93d/F5DGgUjjz9BPf4d9XmqPoZ/R7cKFC5o/f76++93vavLkyZo3b57+53/+x/tg88CBA737xsbG9njg2Wr9+gFHt9ut/Px87+ML586d08WLFwNS41WDYsuWLVc96LXXXtMPfvCDgBQARAp/h2D6P+xTYuhndLNarTp16pScTqcef/xxxcfH680331RHR4fcbvdl+0+bNk2rV6/WI488okGDBvUYLZqRkaFt27YpPT1dsbGxeuaZZzRw4ECtWbNGVqtVXV1dftd51aD4v//7P79PCkQjf4d+9sdhn+ibmTNnavny5ZowYYLuuecexcfH65ZbbtHNN9+sxsZGxcfH99g/PT1dDzzwgP7hH/5BiYmJmjBhggYMGCBJ+qd/+ic999xzmjt3rlwul2699VYtX75ckpSZmal169ZJ6n6I2ldXDYo/fZIaABB4//qv/9rrPhUVFd4///GPf5TVatVbb70lqfvKz6XLS4mJiVq5cuUVzzFjxoxrWmOo13sUf/jDH7Rp0yadP39eHo9HbrdbJ06c0L59+/xuFADgu2984xv62c9+ppKSElksFo0YMULPPvts0NvtNSgKCwt13333ac+ePZo/f7727t2r6dOnB70wAEBPSUlJevHFF0Pebq9BYbFY9Oijj+rs2bO68cYblZ2drXnz5oWiNvRjNTXvqLp6/xV/dunm8KVr/38uI2Oapk5lYkkgUHp94O66666TJI0dO1ZHjx5VYmKiYmLMWeMBkKTk5GQlJyebXQbQb/Tao7jjjjv0+OOPa8mSJVq4cKEaGhoUG9unSWcBv02dmkmvAAgTvXYN8vPzlZeXp2984xvKz8+X2+3udb1sAED06NM9ihEjRujDDz+UzWbTfffd12PKcQBA3ywvWKGW060BP++woSlaV7S61/3Ky8v1n//5n+rq6tL3v//9Ps/91GtQbNiwQVu2bNGwYV8/VWqxWLR3r+8rWAFAf9ZyulXnr5/S+44+n/dAr/s0Nzdrw4YN2rlzp+Lj4zV//nxNmTJFN998c6/H9hoU5eXlqqqq6hEUAIDIUltbq29+85tKSUmR1P0QXmVlpX7yk5/0emyv9ygGDx5MSABAhDt16pRsNpt32263q7m5uU/HXrVH8cEHH0iSbrvtNq1Zs0bZ2dk9RjtNnDjR33oBACHmdrtlsVi82x6Pp8e2kasGxaJFiyR1X9e64YYbeqx9feHCBdXV1flbLwAgxIYPH6733nvPu+1wOGS32/t07FWDYseOHZKk3Nxcbd261Zs+nZ2d+sd//MdrLBkAEEp33XWXXnrpJZ05c0YDBgzQb37zmz7PE3XVoHjyySe9KyWlp6d7X7dardc0CyEAIPRuuOEGPfHEE8rNzVVnZ6fuv/9+3XHHHX069qpBsXnzZkndC4Az5TgAXLthQ1P6NJTVn/P2RXZ2trKzs30+f6/DYwkJAAiMvjwUF46Y3Q8AYIjZ/QCEDNPHRyaCAkBYYOr48EVQAAgZpo+PTNyjAAAYokcBRCij6/0S1/wROAQFIk5r61lt3PiSfvzjxUpOTjG7nLDFNf/ws7rwabWdaQn4eZOHDNOKNX17lMHpdGr+/PnauHGjRo8e3adjCApEnPLyXTp69CPt3r1TDz30sNnlmIbr/ZGn7UyLFoz3BPy8rzX0LXwOHjyowsJCNTQ0+HT+fhEUrvZWnW/0baEld1e7JCkmNtGv9iSmZg+G1tazqq7eL4/Ho+rqdzR7dg69CqCPSkpKtHLlSi1btsyn44IaFH/exdm+fbu2bt0qi8Wiv/zLv9SqVasUHx+vw4cPq6CgQOfOndPkyZO1atWqHlOaX4sxY8b5ddyl67tjx/jzC3+Y3+3CWHn5Lrnd3d/I3G53v+9VAL4oKiry67igBcWfd3E+/fRTbd68WTt37tR1112n5cuX67XXXlNeXp6WLl2qNWvWKC0tTfn5+SopKdGCBQsCUseCBbl+HXfpBuBTTz0TkDoQGHV1NXK5uiRJLleX6upqCAogyII2PPZSF+fSfOfx8fFauXKlkpKSZLFYdMstt+jzzz9XU1OT2tvblZaWJknKyclRZWVlsMpChEtPnyqrtfv7jdUaq/T0qSZXBES/oPUo/ryLM2rUKI0aNUqSdObMGW3btk1r1669bHk+m83W5+X5/tTQoUnXVvCfiYuzflXPoICeN1zb9Veo683Le0g1Ne/I5ZKs1hj94Ae5Gjw4vN+rSPs37U/4t+mbkN/Mbm5u1iOPPKJ58+ZpypQpqq+v93t5vj91+rTTe+06EDo7XZIkh+PLgJ0znNv1V+jrjdPUqZnat2+vpk7NVFdXbNi/V5H2b9qfBPPf5krhkzxkWJ9HKPkieUhwB8+ENCg++eQTPfLII3rooYf08MPd15WHDx8uh8Ph3aelpaXPy/Ohf8rOnqumphOaPTvH7FIAn/T1WYdg+9OlrfsiZFN4OJ1O/fCHP9SSJUu8ISF1X5JKSEhQfX29JKmsrEyZmYwNx9WlpAzW8uUrGBYLhEjIehSlpaVqaWnRli1btGXLFknSt7/9bS1ZskTFxcUqLCyU0+nUxIkTlZvr30glAEDgBT0oLnVx8vLylJeXd8V9UlNTVVpaGuxSgH6FqU4QKMweC0SpP53qBLgWBAUQhVpbz+p3v9snj8ej3/1un9raWk2tB5GNoACiUHn5LnV1dT/B3tXVRa8C14SgAKJQbe3vDLcBXxAUQBS6NM3J1bYBXxAUQBQ6f/6c4TbgC75mAFFo5MhR+vzzph7b/ZXRkrEsF9s39CiAKPToo4/12F648CcmVRLekpOTWTK2D+hRAFFo7Njx3l7FyJGj+vVCWiwZe+3oUQBR6tFHH9OAAQPoTeCa0aMAAiAcr4OPHTte//EfmwN+XvQ/BAUQZFwDR6QjKELM6JunxCiMSMV1cEQzgiLM8O0TQLghKEKMb54AIg2jngAAhggKAIAhggIAYIigAAAYIigAAIYICgCAIYICAGCIoAgzra1ntW7darW1tZpdCgBIIijCTnn5Lh09+pF2795pdikAIImgCCutrWdVXb1fHo9H1dXv0KsAEBYIijBSXr5LbrdHkuR2u+lVAAgLBEUYqaurkcvVJUlyubpUV1djckUAQFCElfT0qbJau+dptFpjlZ4+1eSKAICgCCvZ2XMVE2ORJMXExGj27ByTKwIAphkPKykpg5WRMU379u1VRkamkpNTzC6JhZYAEBThJjt7rpqaTkRMb4KFloDoR1CEmZSUwVq+fIXZZXix0BIA7lEAAAwFNSicTqfuvfdenThxQpJUW1ur7OxsTZ8+XRs2bPDud/jwYeXk5GjGjBkqKChQV1dXMMsCAPggaEFx8OBBPfjgg2poaJAktbe3Kz8/X6+88oreeustvf/++9q/v/sm6dKlS7VixQrt2bNHHo9HJSUlwSoLAOCjoAVFSUmJVq5cKbvdLkk6dOiQxo0bpzFjxig2NlbZ2dmqrKxUU1OT2tvblZaWJknKyclRZWVlsMoCAPgoaDezi4qKemyfOnVKNpvNu22329Xc3HzZ6zabTc3NzT63N3Rokv/FXkFcnPWregYF9LwAEGlCNurJ7XbLYrF4tz0ejywWy1Vf99Xp007vPEmB0NnpkiQ5HF8G7JwA+o9o+pIZslFPw4cPl8Ph8G47HA7Z7fbLXm9pafFergKiAWuMINKFLCgmTZqkTz/9VI2NjXK5XKqoqFBmZqZGjRqlhIQE1dfXS5LKysqUmcm4fUQP1hhBpAtZUCQkJGjdunVatGiRZs2apRtvvFEzZ86UJBUXF2vt2rWaOXOmzp8/r9zc3FCVBQSVmWuM0JNBoAT9HkVVVZX3z+np6dq9e/dl+6Smpqq0tDTYpQAhd6U1Rh566OGQtX2pJxOqNhGdeDIbCCKz1hhhtUQEEkEBBJFZa4ywWiICiaAAgsisNUZYLRGBRFAAQXRpjRGLxRLSNUZYLRGBRFAAQZadPVcTJvxFSNcYYbVEBBJBAQTZpTVGQrlioVk9GUQnFi4ColSkrZaI8EVQAFEq3FZLROTi0hMAwBBBAQAwRFAAAAwRFAAAQwQFAMAQQQEAMERQAAAMERQAAEMEBQDAEEEBADBk8Xg8HrOLCITTp53ehVr6qqbmHVVX77/izz77rFGSNHbsuCv+PCNjmqZOzfStSAD9hs02yOwSAoa5nq4iOTnZ7BIAICz06x4FAARLNPUouEcBADBEUAAADBEUAABDBAUAwBBBAQAwRFAAAAwRFAAAQwQFAMAQQQEAMERQAAAMERQAAEOmBEVZWZmysrKUlZWl5557TpJUW1ur7OxsTZ8+XRs2bDCjLADAFYQ8KC5cuKCioiJt3bpVZWVleu+991RVVaX8/Hy98soreuutt/T+++9r//4rT/8NAAitkAeFy+WS2+3WhQsX1NXVpa6uLiUlJWncuHEaM2aMYmNjlZ2drcrKylCXBgC4gpCvR5GUlKQlS5bonnvu0YABA/Q3f/M3OnXqlGw2m3cfu92u5ubmUJcGALiCkAfFkSNHtGPHDv32t7/VoEGD9OSTT6qhoUEWi8W7j8fj6bHdF0OHJgW6VACATAiK6upqpaena+jQoZKknJwcbd68WVar1buPw+GQ3W736bwsXAQgnLBw0TVITU1VbW2tzp8/L4/Ho6qqKk2aNEmffvqpGhsb5XK5VFFRocxM1qMGgHAQ8h5FRkaGPvzwQ+Xk5CguLk633367Fi1apKlTp2rRokW6ePGipk2bppkzZ4a6NADAFbBmNgAEAZeeAAD9BkEBADBEUAAADBEUAABDBAUAwBBBAQAwRFAAAAwRFAAAQwQFAMAQQQEAMERQXEVr61mtW7dabW2tZpcCAKYiKK6ivHyXjh79SLt37zS7FAAwFUFxBa2tZ1VdvV8ej0fV1e/QqwDQrxEUV1Bevss7E63b7aZXAaBfIyiuoK6uRi5XlyTJ5epSXV2NyRUBgHkIiitIT58qq7V7TSerNVbp6VNNrggAzENQXEF29lzFxFgkSTExMZo9O8fkigDAPATFFaSkDFZGxjRZLBZlZGQqOTnF7JIAwDQhXzM7UmRnz1VT0wl6EwD6PdbMBoAgYM1sAEC/QVAAAAwRFAAAQwQFAMAQQQEAMERQAAAMRc1zFJeepAYABFbUPEcBAAgOLj0BAAwRFAAAQwQFAMAQQQEAMERQAAAMERQAAEMEBQDAEEEBADBEUAAADBEUAVZeXq5Zs2Zp+vTp2rZtm9nlhDWn06l7771XJ06cMLuUsPTyyy8rKytLWVlZev75580uJ2z9+7//u2bNmqWsrCxt2bLF7HKiEkERQM3NzdqwYYNee+01vfnmm9q+fbs+/vhjs8sKSwcPHtSDDz6ohoYGs0sJS7W1taqurtauXbv05ptv6oMPPtDbb79tdllh591339Xvf/977d69Wzt27NDWrVt17Ngxs8uKOgRFANXW1uqb3/ymUlJSNHDgQM2YMUOVlZVmlxWWSkpKtHLlStntdrNLCUs2m03Lly9XfHy84uLidNNNN+nzzz83u6yw87d/+7f6xS9+odjYWJ0+fVoul0sDBw40u6yoEzWzx4aDU6dOyWazebftdrsOHTpkYkXhq6ioyOwSwtqECRO8f25oaNCvf/1rvf766yZWFL7i4uL04osv6uc//7lmzpypG264weySog49igByu92yWL6e7tzj8fTYBnx19OhRPfzww1q2bJnGjx9vdjlha/Hixaqrq9PJkydVUlJidjlRh6AIoOHDh8vhcHi3HQ4Hl1bgt/r6euXl5emf//mfNXfuXLPLCUuffPKJDh8+LEkaMGCApk+fro8++sjkqqIPQRFAd911l+rq6nTmzBlduHBBv/nNb5SZmWl2WYhAJ0+e1GOPPabi4mJlZWWZXU7YOnHihAoLC9XR0aGOjg7t3btXd955p9llRR3uUQTQDTfcoCeeeEK5ubnq7OzU/fffrzvuuMPsshCBNm/erIsXL2rdunXe1+bPn68HH3zQxKrCz7Rp03To0CHNmTNHVqtV06dPJ1iDgBXuAACGuPQEADBEUAAADBEUAABDBAUAwBBBAQAwRFAAAAwRFAAAQzxwh36rtLRUW7ZsUUxMjAYPHqycnBxt27ZNI0eO1LFjx5SYmKh169bppptuMrtUwFQ8cId+6ciRI8rLy9OuXbs0YsQIvfrqq3rjjTfU2NiorVu3avLkyXr99df1q1/9Sjt37jS7XMBUXHpCv1RXV6eMjAyNGDFCkpSXl6dVq1YpNTVVkydPliTNmzdPhw8f1tmzZ80sFTAdQYF+yWq19pgCvr29XceOHZPVar3ivkB/RlCgX5oyZYrq6up06tQpSdIbb7yhF154QUeOHNGRI0ckSdu3b9df/dVf6frrrzezVMB03KNAv1VWVqbNmzdL6l56dM6cOVq/fr1SU1PV1NSkIUOGqKioSKNHjza5UsBcBAXwlQMHDujZZ59VRUWF2aUAYYVLTwAAQ/QoAACG6FEAAAwRFAAAQwQFAMAQQQEAMERQAAAMERQAAEP/H4YoUqM+13nMAAAAAElFTkSuQmCC\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "#to see the effects of random and closely correlated couple of features I created some graphics\n", + "\n", + "\n", + "sns.catplot(x=\"sex\", y = \"age\" ,hue=\"target\",kind=\"box\", ax=1,data=PatientData)\n", + "sns.catplot(x=\"target\", y=\"sex\", height =4 ,kind=\"violin\", ax =2, data=PatientData)\n", + "sns.catplot(x=\"target\", y=\"chol\", kind=\"swarm\", data=PatientData)\n", + "sns.catplot(x=\"target\", y=\"cp\", kind=\"swarm\", data=PatientData)\n", + "sns.catplot(x=\"cp\", y=\"thalach\",hue=\"target\", kind=\"box\", data=PatientData)\n", + "sns.catplot(x=\"target\", y=\"slope\", kind=\"swarm\", data=PatientData)\n", + "sns.catplot(x=\"target\", y=\"oldpeak\", kind=\"swarm\", data=PatientData)\n", + "#Ill " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sn\n", + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize = (7, 5))\n", + "sn.countplot(x ='target', data = PatientData,palette =\"BuGn_r\" )\n", + "plt.show()" + ] }, { "cell_type": "markdown", @@ -436,10 +470,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train set: (242, 13) (242,)\n", + "Test set: (61, 13) (61,)\n" + ] + } + ], + "source": [ + "X = preprocessing.StandardScaler().fit(X).transform(X)\n", + "from sklearn.model_selection import train_test_split\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " X, y, test_size = 0.2, random_state = 4)\n", + "print ('Train set:', X_train.shape, y_train.shape)\n", + "print ('Test set:', X_test.shape, y_test.shape)\n" + ] }, { "cell_type": "markdown", @@ -451,10 +501,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "#I standardized at the begining\n" + ] }, { "cell_type": "markdown", @@ -466,10 +518,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0.8688524590163934" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logreg = linear_model.LogisticRegression().fit(X_train,y_train)\n", + "logreg.fit(X_train,y_train)\n", + "y_pred = logreg.predict(X_test)\n", + "logreg.score(X_test,y_test)" + ] }, { "cell_type": "markdown", @@ -481,13 +549,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "details of confusion matrix =\n", + " precision recall f1-score support\n", + "\n", + " 0 0.81 0.88 0.85 25\n", + " 1 0.91 0.86 0.89 36\n", + "\n", + " accuracy 0.87 61\n", + " macro avg 0.86 0.87 0.87 61\n", + "weighted avg 0.87 0.87 0.87 61\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix,classification_report\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "conf_matrix = pd.DataFrame(data = cm,\n", + " columns = ['Predicted:0', 'Predicted:1'],\n", + " index =['Actual:0', 'Actual:1'])\n", + "plt.figure(figsize = (8, 5))\n", + "sn.heatmap(conf_matrix, annot = True, fmt = 'd', cmap = \"Greens\")\n", + "plt.show()\n", + " \n", + "print('details of confusion matrix =')\n", + "print (classification_report(y_test, y_pred))\n", + "#Accuracy of the model is satisfying for this small dataset" + ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -498,6 +605,13 @@ "\n", "If there are any part which is not clear to you, you can write down your questions here." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -507,7 +621,7 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -521,7 +635,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" } }, "nbformat": 4, diff --git a/task3-knn/KNN-K-means.ipynb b/task3-knn/KNN-K-means.ipynb index 06b0bca..f03f446 100644 --- a/task3-knn/KNN-K-means.ipynb +++ b/task3-knn/KNN-K-means.ipynb @@ -53,7 +53,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -66,8 +66,10 @@ ], "source": [ "IrisData = pd.read_csv('iris.data',names=['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width', 'Class'])\n", + "\n", "sns.scatterplot('Petal Length', 'Petal Width', data=IrisData, hue='Class')\n", - "plt.show()\n" + "plt.show()\n", + "\n" ] }, { @@ -84,7 +86,15 @@ "outputs": [], "source": [ "x = IrisData.iloc[:,:-1].values\n", - "y = IrisData.iloc[:, -1].values\n", + "y = IrisData.iloc[:, -1].values" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ "x_train, x_test, y_train, y_test = model_selection.train_test_split(x, y, test_size=0.2)\n", "\n", "scaler = preprocessing.StandardScaler()\n", @@ -94,14 +104,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Test Accuracy: 1.0\n" + "Test Accuracy: 0.9666666666666667\n" ] } ], @@ -116,7 +126,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## **Exercise**\n", + "# **Exercise**\n", "We have gathered 5 unknown samples below. Predict which sample belongs to which category.
\n", "*Hint: You can use classifier.predict(x) method.*
\n", "**Extra:** Indicate the probability of prediction.\n", @@ -133,10 +143,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Iris-virginica', 'Iris-virginica'], dtype=object)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = [[4.5, 3.0, 1.5,0.5],[7.0,3.0,5.0,1.5]]\n", + "classifier.predict(m)" + ] }, { "cell_type": "markdown", @@ -161,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -246,7 +270,7 @@ "4 5 Female 31 17 40" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -265,12 +289,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -290,12 +314,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -331,12 +355,268 @@ "**Extra:** Instead of using 2 variables and a 2D plot to cluster the data, use 3 variables and a 3D plot." ] }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 0\n", + "1 3\n", + "2 2\n", + "3 3\n", + "4 0\n", + " ..\n", + "195 3\n", + "196 2\n", + "197 3\n", + "198 2\n", + "199 3\n", + "Name: Cluster, Length: 200, dtype: int32" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "MallData.columns\n", + "MallData['Cluster']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(MallData[['Annual Income (k$)']], MallData['Spending Score (1-100)'], 'g.')\n", + "plt.xlabel('Annual Income (k$)')\n", + "plt.ylabel('Spending Score (1-100)')\n", + "plt.show()\n", + "#there should be max max 10 clusters, so we should implement elbow method to determine cluster number\n" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import sklearn.cluster as cluster\n", + "wss = list()\n", + "K = range(1,10)\n", + "for k in K:\n", + " kmeans = cluster.KMeans(n_clusters= k, init=\"k-means++\")\n", + " kmeans = kmeans.fit(MallData[['Spending Score (1-100)','Annual Income (k$)']])\n", + " wss_iter = kmeans.inertia_\n", + " wss.append(wss_iter)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Clusterswss
01269981.280000
12181363.595960
23106348.373062
3473679.789039
4544448.455448
5637239.835542
6730259.657207
7825029.253425
8921913.929201
\n", + "
" + ], + "text/plain": [ + " Clusters wss\n", + "0 1 269981.280000\n", + "1 2 181363.595960\n", + "2 3 106348.373062\n", + "3 4 73679.789039\n", + "4 5 44448.455448\n", + "5 6 37239.835542\n", + "6 7 30259.657207\n", + "7 8 25029.253425\n", + "8 9 21913.929201" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "my_centers = pd.DataFrame({'Clusters': K, 'wss': wss})\n", + "sns.scatterplot(x='Clusters', y = 'wss', data = my_centers, marker='+')\n", + "my_centers\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ı didnt get which point is the elbow is there any way to figure it out but its like 5 so ı wil scatter my clusters based on five clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(my_centers['Clusters'],my_centers['wss'],'r-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I didnt understand why there is a breakdown at point 3" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6/ejmmlqSbThy0qJCZm0jUJ00ozQnSpY3pyyu7arG3VVhwwDQWdvMwfe3M+/yU1TQxQRAZUiPM3qzGWOSXRmGSYQlOYmM8jyS8zJG3TCAFpa69Lqzwls2OoOehVetIqVg+H6NzKkFlK+YEz5OL81lyqkD50P00hKj7lH9jsMRCYGKxEWtHBSKMcTv9dPV0IbP48OelYI1JWlE5s0sz+PMH1xNd6sTs82i+TVGwDBZU5JY8OmVTFk5j6A/gD07FXNSfFVfM6fmc+DdbRGyvLll4540N9HxdLlwNrah0+tx5KQNmJ8yHJRxUCjGCE+XVtxu92sbQUJSVjLLv3QhqYVZIzK/Lc0xaNe6Y8FgNh5TtdbsikKKllRQtWEPoK06Sk+apbaUhoGzoY2PHniJtsMNABQvnc68y07BlmYf8Wsp4zAOBAN+Ah43AoHObImrsYti4tN2uIHdr24MH3c3dbL92Q856cbz0I9zjaLRwJbmYMm1ZzJ99WItWik7NaJMuWJoyKDkwPvbw4YBoHL9bvLmlVGydMaIX2/y/UYmOAGPm+6aSvxdWnSJMTkVW34RepPyOUx2upqiO7c17q7C0+3CZpqc/TaMFhPpJdFObcXQ8Xu81G07GCVv2V83KsbhuHRIBzwefN1dBDxjX/zM29EeNgwAvs52fM7oh8bRBDzukM6eQccqEpOkGA19MqcWYOqn+qtC0ReD2UTOrJIoeXrZ6NS2Ou6Mg8/ZQee+HTj376Jj7w48HW1IKQc/cQSQUuLrbI/WqTM6Rr3vOZ72Njr2ajp37tsRlzFRJB5pJdlMWTUvfGxNtTP3kpOPm37PiuEhdIIpK+aQ3Kc/dv6CKXH1OT+m643VgzHiokJ8E7gRrX7ENuB6wAb8GygFDgFXSCmjax33YcmSJXLDhg1xXzfg8dC5b0dk0TMhSJ42C4NlbPZCXQ21uBpqI2TW/CKsmbGX3n63i869OyIzqfUGkqfOVOGvExC/x0tnfRt+jxd7VuqoOJAVkxt3Zw/OhjZ0Bh2OnHRMtqE/B4QQG6WUSwYaM+YrByFEAfANYImUcg6gB64CbgNel1JOA14PHY8oQb8vuhqmlAR93pG+VL8YU9PRW49kyuptdkyO/vsHB33eCMMAIAN+gn4VKz4RMZi1PfjsiiJlGBTHhCXZRta0AjLK8o7JMMTLeDmkDYBVCOFDWzHUAj8AVoU+fwh4C/j+SF5UZzCATgfB4FHygZf1MhAg4NX2+vUmM2IY0UUGswVH6TTN3yG0MhoDXT/mZzqddi8KhWLE6G7txN3ZgyU5iaR0ZbjH/AkjpawRQvwKqARcwCtSyleEEDlSyrrQmDohxNADqwdBZzKTVFhKd9XB8Nu4raAE/QBVUANeD676WrztLQCYUtKw5hUOK7pIZzTGXWBPb7ZgKyimpyZU3lsIkgrLBtRZoVAMjfodh1j7wMt4ulyYHVaWXX8OuTGcv8cTY24chBBpwMVAGdAO/EcIce0Qzr8JuAmguLh4qNfGlJKG3mIl6POG6hlZEANUyPQ5O8OGAcDb0YYhyY6+Hx/BSCN0OsxpmRhsdq0Gk9GkDMMExNPloqfVicFiwp6VohLBEghnYzsf3PsC/lBVW4/TxYf3rmH1jz4zaNvYycx47E2cCRyUUjYBCCGeBE4GGoQQeaFVQx7QGOtkKeW9wL2gOaSHenEhhOZ8jtMBHSu6yNvRhmWMjANoBsJgVaWOJyrt1U2s/dvLdNQ0ozcZWPjplRQvm4lhEia+TURcbc6wYejF5/bS09Z1XBuH8QhlrQROFELYhPb6dAawE3gWuC405jrgmdFWRAaD+N0u/G4XMhi7baMhKXrvMZZMMbnw9rhpr26iq6l9WKHOPreXzY+9HW4CFPD62fDP12mvbhrkTMVYYXbYoupQ6Qx6LI7jO5s77lcXIUQS4JZSDqv5rZRyrRDiceBjwA9sQlsJ2IHHhBA3oBmQTw/nOoMR9HpxNdXhadH+SE2pGVhz86N8CUZHMt42SzhhTmcyY0xOHU3VFONMR20LG/7xGi0H6tCbDMy//BRKT5yJwTz0Ameezh4a91RHybub2skszxsJdRXDxJGTxqKrT2fjP19HSokQgsVXn4Y9O228VRtX+jUOQggdWojpNcBSwAOYhRBNwAvAvVLKvcdyUSnlj4EfHyX2oK0ixgRfd2fYMCB0CJ3A39ONEAKd8chDwO/qwZBkx5SajpQS6fcTcHVjtI1MNU1FYuH3+tn+zAfhctMBr5+PH3mTlPxMsqYVDHk+o80cs9eCJVn9/iQKOr2OkmUzSCvJxtXejS3NjiM3fUzKrScyA939m8AUtBDTXCllkZQyGzgF+Ai4YyiO5ETD16llGQudHlteAT5nJ92VB+jYtxOfs095i442PK3NuBpqcTfW4WltwtveOl5qK0YZj7OH2m0HouRdTe3HNJ/ZbmXBp1eiNx55Dys5cWZElqti/NEbDaQVZZM/t4zUwiz0BlUMc6BtpTOllFGZVlLKVuAJ4AkhxITN+9fbkqCjDVN6Bq6GOmTAD4D0+XAe3kfKtFnozRYMSfaochUG28iXxx0pgj4vQb9fC5cdJH9DEY3RaiKlIJP2UHvMtOIspp2xCGtKUigGfmiBAT63l4MffsL01YuQEvQGPc37a+lp7RxyLwcpJd0tHQR9AWzpDgxmkyZr7iDoD2DLSFalOBQjRr/GodcwCCFygAK0Uhe1UsqGo8dMRIzJqXhamxE6fdgwhAkGCXi96M2W0LgWgt6Qz8FowpyWeG99Ukr8XZ10VR9C+nzozBbsRaUJbcgSEZPNwsIrVvHOH54if94UMspy2fToW/hcHjKnFjD/U6eQURp/oTNPZw/VG6N3X0uWzSCjLH6fg9fl5fBHO9j29Pv4PT7y509h7qXLafjkMNueeZ+A10/BginMu+wUHNmpcc+rUPRHv9tKQoiFQoiP0DKVfwncCbwthPhICLFojPQbNQxmC46yCoxJDogRc96bgWywWHGUV+Aom4a9dCqOKdPRj1EdpqEQ8LhxHtqH9Gn2Ouhx03X4wJiWBpksZE0rYPUPP0Px0go2/+dtfC4tO755Xw071qzF44q/Mm5QypjVWM1D9Dm0Ha5n07/fCrfYrN2yn5b9tWz+z9sEvNrLTc3m/ex7ewvBoyoAKEYft7OHzroWPF2u8VZlxBhoW+lvwJeklGv7CoUQJ4Y+mz+aio0FepMJndGILb/oSAYyYM0tjEg005tM6E2j04pvpAh6o2swBX1eAj5vhINdER/JuenU7zgcJa/ffghXqxNzwcAZ8n6Pj6qNe9j2zAfMOGsxW596n6BfC/QrPmEGqYVD6+/cu83Vi8FiCofH9qVq/W5mnr1EObzHkMY91ax/+BW6mzpJzktn6edWD2lVmKgMZBySjjYMAFLKj0JhrZMCIYSWgWxNIuDzojcY0VusA2ZN94eUkmCoBpPQ6QkG/OgMxrjqIB05V6AzmfrNoJUyqBmCUFRV77iY1xA6dHqVaHWsmO3R/gV7TipGm4WeNicBrx9rmj3mPn9bZSPr//4qALtf28iiq0/DnGTF7LDiyEkbckc021G1fgJeH9bU6C3DlMJMDKPUU1gRjbOxnffveRZfKImus66V9//8PGfedtWEL6w40JPjRSHEGuDvQFVIVgR8DnhptBUbS4ROh8GWhIFjt3lBnxd3cyOe1masOXm4W5oIetzozBaSCksxJvW/9x/0eXE3NeBu0ZLCLdl5mNOz0B9Vfyng8+IJjxNYs3Mxp2ehM2plQCzZebgb68LjbQVF6FSHuWMmrSSbnFnFNOzQVpU6g54FV6ykeW81mx97G0+XS9vnv3QFjpzImPjO+iMRbVNPnc/htbto2lONJdnGwqtOI39e+ZAiYjLK8siaVkDT3hpAi67JnFpAxpR8WvZrJeANZiNzLjxJOaXHkO6WzrBh6MXd0U13q3PyGgcp5TeEEOei1UEqAARQDfxRSvnCGOk3rgT9fu3tX28Y9O3f5+zE3VSPJTMnIvpJ2/vfp/Vf6OdB7e1sx918pC+su6EWvdmCPjU98hodbX3GSVyhcabUdIRejyUrF6MjmaDPr22FWayqhs8wSM5JY/FnTqe9qhlvj4fk3DSQkncfeDo8xtvjoXl/LXqTHltacljeG9WUPb2Iht1VNIUS4dydPXx43xrO/P5VpA/BsW1Ld3DijefRUduC3+MlOTed5Nx0Tr7pfDpqmvF7fWGZYuww2y3ak7HPjq7OoMc8iqW0x4oBn3hSyheBF8dIl4TC39NNd/VhAu4erTLqIG//nt7cB50uKvpJ+v0Evd6YxkFKibctOm/C19GGuY9xkMEgnraWqHHezg5MoXE6vR6dKu0xotgzU7FnpoaPD37wSfj72ReeSPP+OtY//CoWR2hFMLdMi5kvziZ7ehEZ5XnsfHFd5KQSnA3tQzIOANaUpKjw11gyxdjhyE1n9gUn8clzH4Zl8y8/ZVJkVw+UIW0AbgAuoU8oK1rNo/snchjrYAS8Xi3yJ9RQJ+Bx03Vo34Dd1wxWG/6uzn7f1PvrASGEQG+14e/pipDrjy60JwR6axIBV89R4xIvcmqy4vf5wyU0Mqfk03qogYaQ07p3RXDG964ioywXW5qDZTecQ3djB4fX7qSn1Rkxl9muKutOZHw+H9XV1bjdbkSZg1lfPQMZlAi9Dp9ex+49u8dbRQAsFguFhYUY42wR0JeBVg4Po5XUvh1tOwmgEK0o3j+AK4d8tQlC0OcJG4ZeZMBPwOvp1ziYUtPxtDXj7WjDnJF1pDQHYM0tGLDMtjk9E297a3jFIYxGTEfVbxJCYEnPxNfRGu5mpzOaMA7QRU4xcjgb29j+7IcYTEYypmj7/7tePqpFrQRnfSsZoYbv1uQkrMlJLLr6dN7/07PIoLb3kD+/nNSirLG+BcUIUl1djcPhoLS0NGG3bqWUtLS0UF1dTVlZ2ZDPH8g4LJJSTj9KVg18JITYM+QrTSCELvZb/kB+B4PVRvKUGQTcLtAJjMlpyIAPndGM3jJwzwiD1Uby1NC5aKsBvSnamBhsSTimziTgcmkrDotV9ZEeAwJ+PzvWrKVqg/ZrX3LiTFIKMrGlO+hu6YwYa4oRhZQ7q4Qzf3A1zvo2THYLqQVZQ860ViQWbrc7oQ0DaC+UGRkZNDUdWwXggYxDmxDi08ATUspg6GI6tGqpbQOcN+HRmy1Ycwtw1deEZZbsvEEfxHqz5Zgb8cR7rsFswaCa/Ywp7o4eKtcf2SY4/NFOajbtY/G1Z7Luby+FVwS5M4tJK45eEej0OtKKskkrGvHmhopxJJENQy/D0XEg43AV8AvgHiFErzFIRSvId9UxX3ECIHQ6zBlZWvc1n1cLFbUm9buiUExuDCYDtoxkupuO1Njye3yYkyycedvVdDa0YbKZSS3MGlfnsN/nx9PZg9FiwpSUOC8Qro5uZDCINdU+IR6ox0p9fT233nor69evx2w2U1payu9+9zsuu+wytm/fPuT5HnzwQc466yzy8/NHQdvBGSiU9RAhv4IQIgMQUsrolMxJik5vQGdXkT8KrRnMoitP4717ngmvErIqCknOz8CWaietePxXBJ31rXzy/EdUf7wPR24aC69cRXZF4bg+jL0uL9Uf72Hb0x8Q8PqYftZiypfPiZm8N9GRUnLppZdy3XXX8eijjwKwefNmGhoaBjmzfx588EHmzJkzJOPg9/sxxJF0Gw9xpQFLKVv6GgYhxOoRubpCMUHImVnMGbddxbLrz2HF1y5m2RfOwZYgDzmf28umx96iasMeZDBIZ20L7/7haTpro0OfRxt3Zzc97V2aM/RADRsefg2Pswe/x8cnz31EzZbocuiTgTfffBOj0ciXv/zlsGzBggUUFRWFjx988EG+/vWvh48vuOAC3nrrLQKBAJ///OeZM2cOc+fO5be//S2PP/44GzZs4JprrmHBggW4XC42btzIypUrWbx4MWeffTZ1dVrC66pVq/jhD3/IypUrueuuu0bsno7VxNwPFI+YFgpFgqPT60gvziG9eOx6h8eLq60rnMXdS9AfoLOhjZSCodVwOlZ8bg81mw9oVWO9PuZcfHJM43Tg3W2UnDgTo3lyZXFv376dxYsXH9O5mzdvpqamJrz11N7eTmpqKnfffTe/+tWvWLJkCT6fj5tvvplnnnmGrKws/v3vf/OjH/2IBx54IHzO22+/PWL3AwPnOTzb30dA4tWsViiOU/QmA0abGV9PZLVYo3XsItmaD9Sx7sGXw8efPPMB085YGDXOkZOK3nB8d1g7mvLycg4cOMDNN9/M+eefz1lnnRU1Zvfu3Wzfvp3Vq7VNm0AgQF7ekeJ+V1458pkFA60cTgGuBbqOkgvghBHXRKFQHBNJGcks/PRK1j30SliWN6d0yJVfh0P9J5EVbL09HowWE5bUJNzt3YBmxCrOWISun4TQiczs2bN5/PHHBxxjMBgiyqm73VqPmLS0NLZs2cLLL7/MH//4Rx577LHwiqAXKSWzZ8/mww8/JBZJSSMfCDGQcfgI6JFSRq1VhBCJkf43DshggKA/gM6gV9FLioShcHEF9uxUnA1tmB1W0oqysTjGLpciKUaRueYDday69XI6qpsJBgKkFGaROkbbXGPN6aefzg9/+EPuu+8+vvjFLwKwfv16enqOVDQoLS3lnnvuIRgMUlNTw7p1WlmV5uZmTCYTl19+OVOmTOHzn/88AA6HA6dTy6yfPn06TU1NfPjhh5x00kn4fD727NnD7NmzR+2eBopWOneAz04dHXUSG7+rB1dDLf4uJ4YkB9bcfAxHl7lQKMYBg8lA5pR8MqeMT9hjzqwSLMk23J3aw1Bv1FNx+qLjphigEIKnnnqKW2+9lTvuuAOLxRIOZe1l+fLllJWVMXfuXObMmcOiRVrPtJqaGq6//vrwquLnP/85AJ///Of58pe/jNVq5cMPP+Txxx/nG9/4Bh0dHfj9fm699dZRNQ5CHtUgZsDBQlwgpXx+1LQZIkuWLJEbNmwYfOAIEPR56dy/O9yvAbTyFY4pMxK+EZAMBJDBAEJvOKY+FQpFPDgb2miraiTo01YJaZO4RMjOnTuZOXPmeKsRF7F0FUJslFIuGei8oUYr/Q+QMMZhLAl4PBGGATSDEfS6E9o4+Hu66WmoJdDTjTE5BWtWbkK2OVVMfBw5aVF9LRQTl6Eah8mb3jgI/VZVTWC/Q8DrwXlwb7ign7ethYDHjaN0Wlzd6RQKxfHLUPcYvjQqWkwA9CYz5ozITFhTRha6BK5zFHC7o3pLBHq6o1ZACoVCcTRDen2UUq4DLUNaSvnq6KiUmAi9Hkt2HkZHCgGPG73ZjN6alNBheUIfy/YLUH6HSYXP7SUYCGJOoHpKiomPypAeAnqjEb0xBZgYPRT0ZgvG5FR8ne1hmSU7V5X5niQEA0Ea91TxyXMf4e7sYdppCyhaUqE6wylGBJUhPYnRGYwkFRTjT8sg4PGgt1oxWJPQKq8rJjqtlQ28+/un6Y043PyftwFJxRmLxlcxxaRAZUhPcnRGE6aUxI2mUhw7rQfqOToUfc/rmyg+YcaYJsApxo+XXnqJW265hUAgwI033shtt902YnOrDOlxIhgIIOg/CmokkcEgQZ8fnck4qevpH28YLNHF60xJFnSGxPWDKUaOQCDA1772NV599VUKCwtZunQpF110EbNmzRqR+VWG9BgTDPjxOTtxN9UDAmt2Hka7Y9SMhLuljZbte+ipbya5rJDU6eWYU1SfislAZnk+ZocNjzNUokHAnItOxjSGBfcU8eFpa8FVXxNqHmbCmluAOW14u/Pr1q1j6tSplJeXA3DVVVfxzDPPjL5xEEIIOUj6dDxjFJH4u5x0Vx6pad91eB/2smmYHCPv5PZ2dXP4pXfwd2u9qZs378TV3ErRmSvQm468dQZ8foROIIQg6A9EfJYIBHw+hE6X0JFh40FyXjqrvnU5zftq8Ha5yZxWQHpJ4pUUP97xtLXQXX0YtG7LBH1e7RiGZSBqamoi+kUUFhaydu3a4Snbh4G2ld4UQjwBPCOlDBeLF0KYgBXAdWgtQx8c6kWFEKnAX4E5gAS+AOwG/g2UAoeAK6SUk6pXtZQSd0t0s29vW8uoGAdPW2fYMPTSXd2At9OJNTOdgMeLs6qO1u17SJ1RjquhGVdjC8nlxaROK8WUPL7NbHwuN87DNbR9shdjko2M+TOx5WaqrbE+pORlkJKn4kMSGVd9TdgwhJFBXPU1wzIOsd7LR/JvY6CwlXOAAPCIEKJWCLFDCHEA2AtcDfxWSvngMV73LuAlKeUMYD6wE7gNeF1KOQ14PXQ8qRBCxMxMFvrRyVaO+aYtRDir21lVR80bH2LLyaRpwzbadx/E09ZJ08bt1H+4iYDPF3NeKSUBn5ZcF/B4w9+PNJ37K6l7Zz3ulnaclbUcXvMm7ub+3xcCfj8yGIz5mQwGR01PhWIggj7vkOTxUlhYSFVVVfi4urp6RPtND+RzcAP3APcIIYxAJuCSUrYP54JCiGTgVODzoet4Aa8Q4mJgVWjYQ8BbwPeHc61ExJyRhbe9DW3BBAiBKW10qlaa05JJysumu64xLMuYU4Ep2U7A56Nl6y4AdEYD/h53xLnOwzV4O7uwZkTWynG3tNO6cy8mhx2dwUD7noPozCYyZleQVJA9YmU5/C43zVt2RchkMEhPYwvWrMifl8/loutwHa07NL0y5k3Hmp0RfotyNbXSsn0PntYOUmeUkVxaiDFJRfMoxgad0RTTEOiMw4siXLp0KXv37uXgwYMUFBTw6KOP8q9//WtYc/Ylrr9kKaUPqBuha5YDTcDfhBDzgY3ALUCOlLIudL06IUTMru1CiJuAmwCKiydeHp7BZscxZTr+rk4QAqM9Gf0olf02WC3kr1pGd10j7pZ2knIyseVmoTPoCQa0pD4AYi1FhYiq4Ort7ObwC2+BEGTMn0Hd+xvDn3XXNFByzkrsRbkjo7wQMaNuYsk69lXS8OEmANzNbTgrayi7eDXWzDTcbR0cev4NgqFVQ/37bfi6XOQsnRuzQq2UEhkMKv+GYsSw5hZE+BwAEDqsuQXDmtdgMHD33Xdz9tlnEwgE+MIXvjCiJbzHo/qaAVgE3CylXCuEuIshbCFJKe8F7gWtZPfoqDh6CCEwJtkxJo3Nfr7JkYTJURYl1+n1ZC6cSXddIz5nN5aMVNwt7eHPM+ZWRPkcPO0d+F1uspfOpX3X/sgJpaS7rnFEjIOruY323QdImzmFho82h+V6swlbduQera/HRcvmnZGqBIK4mluxZqbhae0IG4ZeWrfvJn3WFEyOyPtzNbXS+slePG0dpM6YgqMkH6NNVbBVDI9ev8JIRysBnHfeeZx33nnDnicW42EcqoFqKWWvW/1xNOPQIITIC60a8oDGfmdQjAi2vGxKLzwDZ2UNmQtmEXB7cLd3kJSXQ1JuVtTbs9BpK4xgIBgz9DZ2Laf+kcGgtkLps3LxdDg5vOZNAh4v1qx0cpbNx9vZhSnZgb0oF3NapONeCIGItcII6derc+R96MOrJRkMInS6qBWGq6kVf88cshbNVg5wxbAxp2WMiDEYS+IyDkKIEmCalPI1IYQVMEgpncdyQSllvRCiSggxXUq5GzgD2BH6dx1wR+jrM8cyvyJ+dHo9SXlZJOXF15TFnJaKJT2V1u27yT1pEbVvrzsyl9FAUn7MncAoAm4vXTX1tO7YhyHJSsbsaWEfgae1nYBH2591NbXiamrF6Eii5PwZmJOj8zMMVgvZS+ZS8+ZHYZnebMKSqflKLBlpGOw2/F1H2jXmLJtPwOWhdtMO3K0dpM0oByGiVhgtW3aRVlGG0aFqFSmOPwY1DkKIL6Lt8acDU4BC4M9oD/Vj5Wbgn6Gw2APA9WiRU48JIW4AKoFPD2N+xShgsFkoOms5XdX1BH0+Clcvp6uqDr3ZhKMon6Q8zThIKQd82+48XB1hWJwHqii7+EysWekx/QD+HjdigFYijtICis9bibulHYPJhDUnA0tohWFKtlNy7kq6q+vxtHfiKM7HkGTj0PNvYLBaMFgt1L2/kdxlC6LmFXodxFh5KBTHA/GsHL6GVktpLYCUcm9/zuJ4kVJuBmK1qBuOwVGMEgGvj+7aRtp27EVvtZA2ayq20Jt+SllRn3Feumsaadu5D0NoXN+oIQC/20Pzph0R88tgkJ6GZqxZ6ZjTUzEm2/F1Hinplblw1oBv7zIQJOjx0VVVjyklCUtGasTnlrSUsLEA6DhQSdbCWbhbO/B1dZM5dwbodRisFvyuI1FbWUvmqKgmxXFLPMbBI6X09v6BCyEMhOMwFccDXVV1VL/xIdbMNHzdPRx+7g1KLzojyjnsrNTyJnrp2F8ZXhGEiREFpYm13y+TI4mSc06lu64Rv8uNOS0FW27WwCuRA5XUvadFTvXUQsfew5RffCaWo8Jwe9EZjTRv3hnevuqpayJ9bgVFZ59Cd20D3nYnjtICknKH9Q6kUExo4jEObwshfghYhRCrga8Cz42uWoqRJuDz01PXSNuu/ehNRlJnTNHe/gdp/BPw+XBW1pK9ZC49dY0YHUmkTi+np74pbBzcbR24Gltp2XpU1FAwSHdtY4RxMJhNZC2aTXUfI6IzGrDmZkac53N2a/4Gmw2CEndLOx37D+NqaiVlagn2ojyMNiu+HjdNHx+1EvEHcDW19Wsc/C532DD00rZzPxlzp5O1YGTq0igUE514jMP3gRuBbWhtQl9AK32hmED01DRQ+cq74eP2vYcpu+gMbDmZA5ylYclIjQgpdR6qoeD0kwDwOrupfOkdzQEc4+0+ZrSQQU/OCfPpaWpBbzJhTnXQ257c29nF4TVvhbd3umsaKDj9JBo+3BQhy1w4i+zFcxAiumSAIcmGztx/fahYOQxCiAH9GgpFovGFL3yB559/nuzsbLZv3z7i8w/42ii0rjDbpJT3SSk/LaX8VOh7ta00gQgGAjRtiXyrR0qclbXhQ097J02bdnD4pbdp27kfX/eR6J6OvYciTw0G8bR1aOe1deBzdtNVVUfKlJKIcUKvx3ZUJJTf7aVh7RYa1m3B2+6ku7qehrVb6Allcbtb2yP2/QG87Z1Rspatu/B1dSMlpM4oD18ve+lcHEV5tO86QPu+w/hckbWlQDN2ektk5dKsxXMw2pV/QTFx+PznP89LL700avMPuHKQUgaFEFuEEMV9i+8pJgmhN25fdw+Vr7yLt12LTu6qrCN1ehl5yxdHjIs49aicBukP4Kys1VYEDc0Y7TZSKkqxZh5VGqTPVL0GBkAGZfSAo/SM8QFCgLutk+ylc9GZTDR9vJ2Ay6PdR1Ud2UvmkrlwVsTqwpyaTOn5p9FxsApPWwcp5cUk5atqporRo33vIRrXb8XX1YPRbiN76TxSp5UOa85TTz2VQ4cOjYh+sYgnaykP+EQI8boQ4tnef6OmkWLE0en1ZC2YGSETOh2O4jxAq97aaxh6ad99EG9nF3qjkcyj9uGFToe9QHuYmtNTwpFEroZmGtZvxZqTQc6JC7BlxU76SZs5JXI+vR5TipatbMlIwXBUVrI5xYHBaomQZcybgdGRhMFqwVGQQ+OG7QR9vrBh6KVp8w58fXIcerFkpJKzZC7Fq1eQMqUYg+qBoBgl2vceovbd9eHfQ19XD7Xvrqf9qBV5ohGPz+H2UddCMerYCnIoOXclbbsPoDMZSasoxxp6eA+2S2gvyqX4nFNp33UAvdVM6vRyLKEVgcmuRRd1HKjSyn2XFWEvyu2/NpEEndlI9tJ59NQ3obeYsWakhff7TQ47JeetouNAJe6mVlKmFGPLz6bk/FV07K/E3dxGypRi7EV54dVAcnkReosZr/PojraE4urULqhi/GhcvxXpD0TIpD9A4/qtw149jCaDGgcp5dtCiBxgaUi0TkqpSltMMPQGA/aiPOxFeWFZ0O+nu6EFX1c3RocdX5+Ha0pFWbi2kt5oxFGcj6M4H0+7k67KGlq27cZRnE9SQQ7mtBSyF8fZj0JAV3UD1oxUzOkpCKFDBoN4u7rDQyzpKVjS50acZrRZsaSnEvD66KlvpP6jzRjtNpLLirBmppFcVoinw6mFqLqPrB4y5s/AaFcZzorxI9bKdSB5ohBPhvQVwJ1oJbQF8AchxHellI+Psm6KUaa7tpHKl94BIchaNBt/jxtPe6f2Zl6cF1V+29fdQ9Wr74V9BZ37K8leMo+M+dPjr2IqJdbMNBrXbsGU4iDo9RH0+yk47cS4TncerokoldH6yV7KLjoTS3oKQggyF8wMO8ltOVkYU1VLVMX4YrTbYhqCRA+AiMfn8CNgqZTyOinl59Cypf97dNVSjDYBv5/m3mqmUtK0cTvOyhpSK8pInzUVU5+37aDfT1dNA87DtWHDYMlMI2fZfDxtHTRt/ARXU2t8FxYCd1MrOSfMx5KZhqO0kMyFs/B1R0cVHY3f5aFxw7YIWdDrw9XUAmi9Jho+2kx3bSMyEKR52y7q39mQ8G9oislN9tJ5UcUhhUFP9tJ5w5r36quv5qSTTmL37t0UFhZy//33D2u+o4nH56A7ahuphfiMiiKRkVp11b74u10x9+27axqofPldshZpteKFXkfKlGIa1m7B6EgiWFtP6/Y9lF50BtbM2IlnR64rsWal07B2S1ikMxopWLUsLqV7o5qMDjtBr5eAxxv2mchQvXyfsxufM7RNpdeBirxWjCO9foWRjlZ65JFHhq/cAMRjHF4SQrwM9GpyJfDi6KmkGAv0RgOZ82dQ/dr7R4RC4CjOp7u2EefhGnQmI8mlhTSGMpCDPj+GJCvWzDR6mlrJWTYfd2sHOqMBY5JNq480iHEQQtC+52CELOjz4evjc+gPg9VCzrL5+Lq68bR2oDebMNgs4XBZS3oqerMpIvs5Y+4MgoEALdt24+lwYi/KIyk3C715eF24FIqhkDqtNKGdz7GIxyH9XSHEZcAKNJ/DvVLKp0ZdM8WoYy/MpWj1Clq278FgMZE+p4KA26P5IUJ42jqQAa2Udesne8laPAdhMCAE1H/wcXiczmggf+Xgb/9SSmQgus9z/O/2ksZ1W8NHxiQbyeVa8T+h05G1eA49Dc34nN0k5WWTlJ9N5YvvhI1P24595J68iIw5FXFfUaE4HonHIV0GvCClfDJ0bBVClEopD422corRRW8yklxWiKMkX+tn4A9Q+eLbEWO6qurIXjqP+g8+RgaDNK7fStqsabgamyPGBX1+Ah6PtuqoqsVgMZNUmBvVg9pgMZO5YCa176wPy4ROR1IcZTz8bg9NG0JlAkLOZxkM0rJ1N46SAoL+APUffIw5NRlDkpXWHfsQBn3UqqRx/TYcpQURfhWFQhFJPNtK/wFO7nMcCMmWxh6umGgcKb4nCfp8EZ8FfX7Q6Sg882Ratu3BYDWTWlFKT10DoG31BAMBZCCA0Ok59Pwb4XN1m3ZQduHpUQXwHGWFFOj1tH6yF2OSjfS5FViyjsqkjoEMSoKhePH02dPo2Hc47Fto27mfvBVaFXhvVzcBn0+7lxj+BhkMKD+EQjEI8RgHg5QyvIkbKt+tNmwnIXqjkYz5MyPKbiMElvQUknKzcJQUaAXqQts3Pmc33s4udAY9low0WrbvjZgv6PXRXdcUZRx0ej1Gu43UGeXo9Hr0JmNcrTiFTpA6rZTmLTvRm01hw2DNSie5rAih15O5cDYEg/hdbkwpDsxpyeiMhogub+mzK1SfBoViEOIxDk1CiIuklM8CCCEuBpoHOUcxQXEU5VF4+km0bN+DzmQkc/7McGnuvrkMOr2BhrVb0JtNyGAQR1lh1KoDiMoMBeiqaaDq5SMVYvVmE6UXnRHRkCcWMijxu9xaraRQbSed0YCjtJCGdVvImD8D58FqvH0aBRWdcwrZJ8ynu7oeX1c3SQU5WHMyBy1VrlAc78RjHL6M1tLzbjSHdBXwuVHVSjHiyGAQV1Mr3bUN6AwGbPnZUf4A0B7UKVNLcJQWgohd3jrg89G29yA5J2iRQ8KgR28xY0mPLO2NEFFVWQMeL40btkbJeuqbBjUORpsFW24WAa8XU7I9bBjadh0AQGcwRBgGAHdzG00btmPJTMOQZKN990Hadh3AmpWOKcGTkBSKgaiqquJzn/sc9fX16HQ6brrpJm655ZYRmz+eaKX9wIlCCDsgpJTOwc5RJB7d9U0cXvNWeK9dZzRQemH/eQk6wwAZz1KSlJdN/fsbwyK92UTBaSeRv2oZrdt2o7dayFwwM7ILHFq0UtCrbfHoTEZkIIgMBAh6olcdMfUyGal9Zx06k5HsJXORUtJVVRfWK1pX7Yu7uQ1oA0I+lmB0xJRCMZEwGAz8+te/ZtGiRTidThYvXszq1auZNWtkGlb1axyEEBcCW6WUh0OibwGXCyEOA7dIKQ/2d64isQgGAlo2dJ+HZ9Dnp6uqLso49K4weuqbtBVGXhbm1OQjMqO26ujYdzjivIDHi6etg8z5M0guK0TodLGb6uh0pM2eighq5Th0BgNCr8MUo8xF0B/A1dxKT30zerMJW24mjeu1VUfQ66P+w03Yi/JInzNNi2ISAp3BQNDvx5KRSsqUEox2W1jWS/qsaQlfukAxuTi8dhfbnnmfnlYntnQHcy9eTsmyGcOaMy8vj7w8rVaaw+Fg5syZ1NTUjL5xAH4KnAgghLgAuBa4GlgI/Bk4e0Q0UIw6MigjitH1EvBEy7rrmjj8wltHVhhmE4WrllH5ynthWUpFaVSbTQAZ0PwLemP/XdhkIIjeZKKuTyir3mom/9QTosZ21dRH+CbS51QQ9EauMLqq6kidUU7u8kUEvX5yTlxAV1UdtrwsGj7ajN5sInvpXJxVdfic3aRNLyd5arHyOSjGjMNrd7Hhn68RCK2Ye1qdbPjnawDDNhC9HDp0iE2bNrFsWTyVBuJjoL8QKaXsLUpzGXC/lHKjlPKvQNYA5ykSDL3REDPpy1FcEP7e19WDs7qOpo8/iVhhWDPTadqyK0LWdbiWtOnlUfPZ8rMH1UXodbR9EhnVFHB5onIR/B5veJXQS+fBKtJmTj1qQoEpyUbG7ArMqQ7q3tuAOSOV1k/2aXN7vNR/uAnp85O3fBGZC2aq/AbFmLLtmffDhqGXgNfPtmfe7+eModHV1cXll1/O7373O5KTk0dkThh45SBCfoYe4Azgnj6fWWKfokhU7MX55K88gZYtuxBGA9mL52DN1vwBfrebmnfWozPoo1YYepMRf1tk4bqAx4vObCTvlCW0bN2tJbYtmh3lX4iFDAQJxPAvHJ01LQPBKF383S7M6cnknrSQ1h37MNgsZC+eo/Wv5kg3uaDHS9AbubLpaWhWBfgU40JPa2w3bX/yoeDz+bj88su55ppruOyyy4Y9X18GMg6/AzYDncBOKeUGACHEQqBuRLVQjDoGi1nbUiktBJ2I2PrxtHbQXV2HzmQkffa0iPadXTX1ZC+ZQ/0HmyLmM6c4SMrLJqW8OGq+AfWwmkmfW0HDh33mEwLbURnSRpuF9DnTaVy3JWKc0W4jpbyYlIoyhE6H3njkV9iclozQ63FW1pJcXkzbzn0R55ozUuPSUaEYSWzpjpiGwJY+vHLyUkpuuOEGZs6cybe+9a1hzRWLfo2DlPKBUMG9bKDPXyj1wPUjroliTIhVcK436zjo9eFp6yRzwSw6D1RqEUGL52LJTEHo9LRs3YXOZCJ7yZxwF7ljKWBnyUglY94MOg9Wo7eYSJ1WGnOe1GmlCJ3QMqltVrKWzAkX2TPEGG9JT6X0gtNo3LgdnVFP5sJZdOw7rFXBXDwnZuiuQjHazL14eYTPAUBvMjD34uXDmvf999/n4YcfZu7cuSxYsACAn/3sZ5x33nnDmreXAUNZpZQ1QM1RMrVqmGSYUx3haqbOQ9X0mE2kzZ5G+qypGEP9nNNnTSW5vEh7WzfFt0qIRcDro2HtFnzObuyFudrxR5vRnbIEc2rkfqkxyUrmvBmkVpQi9Pq4VicGm5W06eX4XW6sWemkz5pG0O/D3dJB6879WNJTsGam43d7cDW24Hd7sGSkYs1Mi2pupFCMBL1O55GOVlqxYsWgLX6Hg/prUGBKdlBy3ioaN27H3dSKo6yQ1KklYcPQi8FiHva1ZCBAwOUm4PZEhMMG3NHRT0euG5+Ly9fdQ9Vr7+Pu03io+JxTady4DXdT2xHZuStpXL81lPugUXj6SaRMLRnKrSgUcVOybMaIRSaNFco4HCcEg0F66pvxtLYjdDosmWnhshig1ScqOnM5Aa8Pg8U0pFBPX3cPrqY2/D0uzKnJWLLS+n3LN1gtpM2aFulLAKw5GTHHH40MBnE1t+FpbceQZCPg9SKDQQwWC/4ed9gwmFIcpFaU4evqiTAMAO6m1gjDAFD/4SZsedkYkyINokJxvBJPye5YIShOKWV8Ka2KhKC7Vqtn1BsVpLeaKV59CrbcI45gnUE/cGZ0DPwuN7XvbqCrsjYsy1u+mLRZU/stpmfLySB9TgWdByrRm82kzSjHYI3Pd9Fd08Dhl94htaIMd+t+PK0dZC+dS+1b68haqCX/CL2etJlTaFi7hawF0QlBsfpJ+N2ecJ6GQqGIr93nx0ATsAfYG/r+oBDiYyHE4tFUTjEyBHx+WrftiXgoBlweumqOuI/8PS6clbW07TpAd20jAW98tt/d2hFhGICQTyG63aimi4/mrbsxJdvJWbaAjPkz8PW46akfuAe1r7uHrpoG6tduBikx2m3aFlhJPu27D4CUCJ0OodORXFpI2879mkyvi1oFGZOTomRp08swqFWDQhEmrjahwFNSypcBhBBnAecAj6HlPoxcSp5iVAgGAvh7XFFyX7cm83s81H24ic79leHPck9aSPqcikFLaceqxBr0+8MRUEcj/QEchbnUffBxOLHO6Egi58QF/V7D1+Oi5q21mJLt+HvcoYto5xqsFrprtN4SrTv2kbNsPgG/H2eVZrBad+4nZ9l82vccwtfVTcq0Umx52RSfu5KGtZvxObtJrSgjffa0mOU+FIrjlXhWDkt6DQOAlPIV4FQp5UfAMXsohRB6IcQmIcTzoeN0IcSrQoi9oa9jFnfo7Oxi3YebePo/L/Dhuxtob+scq0uPCUaLOaaz1V6YC2h5Dn0NA0DDuq14OwdP0jGlJCOO2oqy5Wdj7CcLWRj0tO89FJFx7XN2E3BFl/Loxd3STndNA13VDSSXhVqChlYEEokj1CbU3+PSVi1dPaRWlGmy7h4a1m7B6Eii+NyV5J64AHOyHXOqg6yFs8lZNp/k8qIRWzUEAwF6Gptp232AzkPV+LpV4p1iYhLPyqFVCPF94NHQ8ZVAmxBCDwyntOUtwE6gN37xNuB1KeUdQojbQsffH8b8ceHz+vjX357gj795ICz77A2f5mvfuQGbbfJsMzhKCwl4/bTt2IvOYCBz4SxseVq5i6PrFYEWVRT0Db4Hb0lLpvS8VdR/tBlPaweOskKyFs7qN9xV+gPhFUtfAt7+o5V6s5393T3YC3KQwSBtew+Re9JC/G4PQZ+P1OnldB6owmCzYE62Y0pLQegEbTsPYEiykjajHGtmGkKn06KaXv8AV/2RtiRFZ63QEgSHSVdlHVWvvhc+tuZmUnTGyaq5kGLEcbvdnHrqqXg8Hvx+P5/61Ke4/fbbR2z+eIzDZ4AfA0+j9XN4LyTTA1ccy0WFEIXA+WjF/XpT+y4GVoW+fwh4i1EwDl3ObnZ9spfamnqyczJJTnHw57seihjz8P3/4bxLVjN73vSRvvy4YU5xkLN0LqnTSkCnw5xsD39mSonulmbNSsfoiK8GkS03i5LzVhHwejFYLQNuzxisFtJnTqFxw7bIOQboIW1OTUbodNiL82na9AlBnx9HcT49jS3ozCYE0FPfROr0MgJuD40bt1N63mmaT2PudIReHxGG625uizAMAHXvb8SanREVvjsUfD0u6vqUMQdw1Tfjbm6bdMahubaF6n3V+H0B8styyS/PH2+VjjvMZjNvvPEGdrsdn8/HihUrOPfccznxxBNHZP54+jk0Azf38/G+fuSD8Tvge0Df/PGc3gQ7KWWdECJmFTchxE3ATQDFxcVDuqjP5+PfDz/NXb+4Nyz7yS+/RyBGlEpXPw7Vic7RiWaazEHJuauo/3AT7pY27CX5ZC+eGzMLuT/0JmPcyXEpFaUEfNoqRm82kXPiggHrMpnTUyk+dyXddY301DcRcHto3b4n/Hnh6hUIIWjduQ+D1ULh6SdhyUpDCBHzoRzL2e7vccfsWjcUpD+A3+WO63oTmYbKRu75/l9orQuFDVtM3Pybr1I6S+WJ9Mf6Vzfw3F9foK2xjbTsNC688TyWrl4yrDmFENjt2kuez+fD5/PF1W43XuIJZa0AvgOU9h0vpTz9WC4YKv/dKKXcKIRYNdTzpZT3AvcCLFmyZEjpgZUHa/jjr++PkH2yZReFRXlUVx2J3EnPSKWwpODo0yc1ttxMSs5bScDnw2Axj2q2sMmeRM4J88iYPQ2h12GwDpzkJoTAXpCDOSMN6Q/QsnVXxOcGi5mcZfNDq4TB5zOnJoMQEX6PlCnFGIa5jWiwWUiZUhzZ60KImAZ5IrPn4z1hwwDgdXt57dE3+Px/fxaDUaVOHc36VzfwyK8ewxcqONnW0MYjv3oMYNgGIhAIsHjxYvbt28fXvva1MSvZ3ct/gE3AfwHf7fPvWFkOXCSEOITmxzhdCPEPoEEIkQcQ+to4jGvEpMvZjf+ot8NnHn+Jn/zyeyxbvgidTseCJXO4+293UBBy1h5P6M0mTPakMSkjIUJF9AZ7kPfFaDGRPnsq6XNCRiXJRuGZy7Fmab6EeOezZKRSfPYpmJLtIASpFWVkLZ4z5ByPo9EZDGQtnkNKRZlWSjzZTvHZp2CZZAX/mutaomQNhxvwTbIV0kjx3F9fCBuGXnweH8/99YVhz63X69m8eTPV1dWsW7eO7du3D3vOXuJ5CvillH8aqQtKKX8A/AAgtHL4jpTyWiHEncB1wB2hr8+M1DV7KSjKjVolWK0WCoryuOu+n9Le1klyigN7nHvtirHH5LCTe+JCMubOQOj1GG1Drx4vdDocxflYszII+v0YbAP7SYaCOcVB/ilLyF48B53BgME6/JIjicb0xRW88e+3ImQnnX8iVpUnEpO2xrYhyY+F1NRUVq1axUsvvcScOXNGZM54Vg7PCSG+KoTIC4WbpveTNT1c7gBWCyH2AqtDxyNKZnYGv/7z/7L4hPkAzJxboa0SivKwJdnIL8xVhmECIHQ6TI6kYzIMfTFYzZgcSSOe36DT6zE5kialYQAom13KVd++AluyDYPJwJlXn86i0xaMt1oJS1p27Kj8/uTx0tTURHt7OwAul4vXXnuNGTNGrn5TPCuH60Jf+24lSSC6FdgQkVK+hRaVhJSyBa2p0Kgyc8407v7bHbS3dpCcYseRMrya6olOZ4eTPTv309zUSkFRLtNmTMEyAgX0FIPT0dJJ7f5aXN0ucopzyC/PG1GH4XhhTbKy/MKTmH3SLIKBAKmZqej0qu1qf1x443kRPgcAo9nIhTcOr7R2XV0d1113HYFAgGAwyBVXXMEFF1wwXHXDxBOtVDZiV0sQkuw2ko6DBvPdXT3c+4e/8/f7HgvLfvKL73LpledPiodUItPW2M7DP/8nezdpAX0Go56v3vllpi2YOsiZE4fUzJTxVmFC0Ot0HulopXnz5rFp06bBBx4j/RoHIcTpUso3hBAxe89JKZ8cNa0SFFePm727D1BX00BufjbTppdj67PPWlNVx97dBwCYNr2cgqK88VKVykPVHNxXGWEYAH7xkz+weNkCSsqGn/B1PODvceNubSfg9WJOTcaclhKXYa3aUxU2DAB+X4An//g0N/7P9TRWN+NxuckpziGv9PgLfDgeWbp6ybCNwVgz0MphJfAGcGGMzyRwXBkHn8/H4488x53/c3dY9s0ffJlrrr8ck9nEvt0H+crnvktDfRMAOXnZ/Onvv2RqxdgvvHZ9spebrvk2V3z24qjPXC73pM3hGGl83S5q310fLiwodDqKz12JvSBn0HOd7dE/49JZJfzrl/9mz6a9gLa18NU7v8TUeVNGVnGFYgTod6NQSvnj0NfrY/z7wtipmBgcPlDFb38WGbR11y/u5dCBKgBeeu71sGEAaKhr5OXn3xxTHUGLe37koSdpb+tAIKL8C8WlheTF8XBTaJnUvYbBYLOQuXAWroZmnNV1+N3RyW59yS2O/hnnluSGDQNo4YzP/PlZXDHKiSgU481A20oDdqyWUv5m5NVJXDranVE5EsFgkPa2DgC2bd4Zdc72LbuiZKONx+NlxzYte/jxR57j5u/eyL8efIKaqnpmzZvO//vZd0hXvZTjotcACL2ezPkzaVi3JVz23FFaSN6Kxf2W2yiqKOTaH3yGJ+9+GleXi/kr5xGro2P9oQY8PR4VBqpIOAbaVuoN45kOLAWeDR1fCLwzmkolInkFOaSlp9DW2hGWJac4yA/5Fc696Aw+fHdDxDnnXHhMSeTDwmazcsGlq9m9Yx8tTa3c/esHOOfC0zj7gtOZM28GyamTOzprJDGHItmSywpp3bk/oh+G81A1aTPKMRbHfqibLCaWnb2UigVT8Xp9pGWlcnhXZdS4hactxJGm/k8UicdA20q3SylvBzKBRVLKb0spvw0sBo47b2Z+YS6/u++nlIbKQxeVFHDXX39GYcg4LF+5jOtuugqD0YDBaODzX7qKk09dOi66nnX+aXzqmovQ6/UEAwEKCvOYOWeaMgxDxJKZRsGqZZhSHHg7osuXx6qjdDRpOWnkFGVjspgonl7EVd+5Aqtdy8+Yu3w2Z151GvphZmYrFKOBkLHWun0HCLELmC+l9ISOzcAWKeW4d8tesmSJ3LBhw+ADR5DWlnbaWtpJS08hPTNye8bn81NbXY8QmjEx9FOGor62kf17DyGEYEpFKTm5WSOup8/ro6a6Hr1eR0FRHroh9IRWROJ1dlH/wSach2si5GUXnRnRZjVeWupb8Xt8pOWkYbLEX9xwvOhs7aTuYD1+n5/ckhwy8uLr9z2Z2blzJzNnzhxvNQDNz7hkyRIKCgp4/vnnoz6PpasQYqOUcsDwqXiS4B4G1gkhnkKLUroU+Hvcmk8y0jNSSe+nVo7RaBg0RHT/3kPcfP1t4RIeU6aV8tt7/y+8IhkpjCbjiM95vGJy2Mk+YR4Br4+eukZ0JiO5Jy3Ekpl6TPNl5I5GgYHRoaWuhYd++k8Obj8IgCPNzld++SWKph13mwcJy1133cXMmTPp7BzZJmWDvk5KKX8KfAFoA9qB66WUPxtRLY4jXnj61bBhmD5rKudcdDobPtzE7h37VOGyBMaSlkLx2acw5VPnMuXyc0ibXj4mBQrHm31b9ocNA4CzrYs3H3ubwDDLmx9vrHn6Vc4++Qrml67i7JOvYM3Tr47IvNXV1axZs4Ybb7xxRObrS7y/3ZuBut7xQohiKWW0d00xIMFgkA0fbQFgakUZy1eewD2/+RtSSnQ6Hbff+X0uvOwstQWUoOhNRvTpx1dWcN2h+ijZoR2H8Lg82ByTv8rASLDm6Ve5/bY7cYda4dbVNHD7bXcCcP4lq4c196233sovf/lLnM7BW/oOlUGfQkKIm4EG4FXgeWBN6KtiiOh0Os65SItgWn3eSh6699/0+nyCwSD/98Nfc/hg9XiqqFBEUD4nOolz4WkLsNpV6G28/P6X94UNQy9ul4ff//K+Yc37/PPPk52dzeLFi4c1T3/Es3K4BZgeKoynGCarVi9nx7Y9+AOBqA50Ho+X9tYOUAmzigShfE4ZZ392Na898gYBf4B5y+dw4nnLVG2uIVBfG7s1TX/yeHn//fd59tlneeGFF3C73XR2dnLttdfyj3/8Y1jz9hKPcagCOgYddZzi8/k5uP8wjfXNZOdmUjalBOMA3bDy8nP4r59+kz07DvCPv/4HV59wyJTUZHLyRj5ySTF0mmubaappxppkJack+7hNUrOn2jn3urNZunoxAX+AjLwMzJO0FPlokZufTV1NQ0z5cPj5z3/Oz3/+cwDeeustfvWrX42YYYD4jMMB4C0hxBogvDY63jKkYxEIBHjx2df58Xd/QSAQQK/Xc/svv8f5l65GP0CPALPZzJwFM/jVn27nv771M9paO8jMTueOu/4f+cdhB7pE4+COQ/z5+/fR4+wBYPmFJ3PBjediT7GPs2bjg96gJydGORBFfHzje1+M8DkAWKxmvvG9L46jVoMTj3GoDP0zhf4pQhw+UMXtt90Z3h4KBALc/oNfMWf+DMqnlQ54rhCCU047kUefv4/WlnaystPJHqV8hwP7D9PS3EZefjYlZUXK4T0Arm4XT979VNgwALz/3AfMP3UuM5eOe2qPIgGRQYnP5yPgD6I36DGaDBHbbr1O59//8j7qaxvJzc/mG9/74rCd0X1ZtWoVq1atGrH5IL5+DrcDCCGSpJTdI3r1CU5Lc3tU+KnP66OluW1Q49BLXkHOqBXC83i8PPno8/ziJ38gGAxiNpv41T23s/LMk0flepMBV5ebyt3RQQHtTWpnVRGNlJJuZzet9VrLTyEgIy8Dq90aZSBG0hiMBfFEK50khNgB7AwdzxdC3DPqmk0AcvIyI/o5ANiSrAnjNzi47zB3/Pj3BINaTSCPx8uPvvUzavv00FZEkpScxPTFFVHyzPzEzAp2d7s5tPMwezftUwZsHPB5fWHDACAltNS14vf5x1GrkSGe/YXfAWcDLQBSyi3AqaOo04ShqKSAO+/+Mcm9BdpSHNx5948pKikYZ800GhuaObo8SmeHk9bW9vFRaAJgtpq4+EsXklOiOQv1Bj0X3XQBhdMS4/+0Lx0tnfzn90/w66/8jt9/84/89ubfU7O/ZvATFSNGwB+MkkkpCcaQTzTiSoKTUlYdFbqm0iMJ+Q1OP4nH1txHc3MbGZlp49r9ze/3c2h/Fa0tbeTkZZOXn4Ner48Imc3ISicrOzHfghOFgin53HLXzbTUtWC2WsguzEyI4njuHg+NlQ14PT6yCjI5vKuSdS8fqS3WWt/KK/94nWt/cDVGk3EcNT0+kFJiMOgRgohy7DqdSIjfFyDq5XAoxBXKKoQ4GZBCCBPwDUJbTAqN/KK8cOnu8cLn9fH8U6/yvz/6NX6fH6vNyu//+lN++tsfcvv378TlcpOalsIvfv/f5OQNL4TueMCRaseRmjjRSZ2tnax54EU+eP4jAPLL81mwcn7UuL1b9uHqcmFMV8ZhNLFYLLS0tJCenk5GXgYt9a3IoESnE2TkZ2AwjX9pFSklLS0tWCyWYzo/njv4MnAXUADUAC8DXzumqylGjQP7D3P7bXeG/QuuHhff+epPeOzFv/LYi/fT1tpOdm4W+aoL3ITk8K6qsGEAqDtYx/ILTowaN3PpDFXWYgwoLCykurqapiat+2PAH0AGJUIvcFaOfCmLY8VisVBYeGxFEuOJVmoGrjmm2RVjRmN9c9gw9NLR3klzYwtzF8watFqsIrFpqIxMopJSUrm7ilMvPYV3n34PKSV5pbmcedXpGAZIwlSMDEajkbKyse8PP5YM+lskhChHWzmciFay+0Pgm1LKA6Osm2II5ORmRfkXUtNSyMicOOWhFf0Tqye1z+fn8hvO46Tzl+Fz+8gsyBjXrnLuHg+N1Y0E/AGyC7JISkkaN10UwyeeaKV/AY8BeUA+8B/gkdFUSjF0yqYUc/ud38dk1vIU7Y4k7rjrv1TG9SShZGYxp166InycVZjF2deeidVuoXBqAWVzSsfVMLQ3tfPYb//DnTf9ht989S7+fNu9NFQOr3aQYnyJpxPcWinlsqNkH0kpozc8x5jx6ASXyAQCASoPVdPa3E52bmbChNQqRgaP20NjVVN4lZCcnjzeKoXZ8NpGHvq/yLo+Z1x1GudcdzbN1c0EAgGyCjKVPyRBGKlOcG8KIW4DHkXbVroSWCOESAeQUrYOW1PFiKDX6ymbUkLZlJLxVkUxCpgt5oTtwHZw+6EomUDwn989Hg63nTKvnM9890qyi1S03EQgnm2lK4EvAW8CbwFfQesMtxFQr+0KRRx0tnZyaMdhGg43TMouasWziiOODSYDjgxHRB7G/q0HWPvyhmHF3ivGjniilSa3S16hGGWq9lbzwI8fpLm2BYNRz0U3XcjJ55+I2TZ5Sl9XLJjK3OVz2Pb+dgCmLZxGw+Fon8O297dz1jVnqLLfE4B+jYMQYilQJaWsDx1/DrgcOAz8RG0nKRSD4+p28fhdT9Jcq/XK8vsCPPnHpymZUUz53Mnz3pWWnca1t32GhkptZZRdlMWOtbv44PkPI8bNWFKB0awS9CYCA20r/QXwAgghTgXuAP6O1vjn3tFXTaGY+HS1d3Fg+8EoeXPd5GusaHNYKZtdytT5U0hOT6Zi4VRmnnCkzHlOcTYnnX+iKhk/QRhoW0nfZ3VwJXCvlPIJ4AkhxOZjvaAQogjNyOQCwdC8d4Uc3P8GSoFDwBVSyrb+5lEoJgI2u42CqQUUlOeRlpNGMBDk4zc3k5qVMqx5nW1O2hrbsdqtZOZnJGTbzvTcdK770bU0VIVyH4qySclInAgrxcAMaByEEAYppR84A7gpzvMGww98W0r5sRDCAWwUQrwKfB54XUp5Ryg66jbg+8O4jkIx7iSlJPGpmy/h0V//h3WvbMBoNnLO584it+TYy5hU7anmb7f/naaaJkxWE5/+xuUsPmNhQhbbS0pJojxl8myfHU8MtL57BHhbCPEM4ALeBRBCTGUYPaWllHVSyo9D3zvRivgVABcDD4WGPQRccqzXUCgSBXePmzUPvBROCPN5fDx33xpa6o7NZdfj7OFfdz5KU41W08fr8vLPXzxC7QHVo0MxsvRrHKSUPwW+DTwIrJBH4s90wM0jcXEhRCmwEFgL5Egp60LXrgNUMLRiwuNsc7Jvy/4oeVNt8zHN19naSfXe6J4NvQ5vhWKkGHB7SEr5UQzZnpG4sBDCDjwB3Cql7Ix3z1QIcROhLa7i4uJBRisU44vVbiOnJDsqrPNY995tdhtp2am0NbaPyHyKiYfX7aWlrgWdXkdm/uj1GhmXsAEhhBHNMPxTSvlkSNwghMgLfZ4HxCzMIqW8V0q5REq5JCsrMdpxKhT9YU9J4spvfhqTxRSWrbjoZAqnHltpk+SMZD7zvasi/AunX7GK/Cn5w9ZVkfi01LXwjzse4WfX/5Kff+FOXnjwZZztXaNyrUFrK434BbUlwkNAq5Ty1j7yO4GWPg7pdCnl9waa63isreTxeKmpqkOn01FYnIfBoMozTwRqD9TSUNmELdlGcUUhVrt18JP6QUpJQ2UjzbXN2FPs5JbmYLEdW0OX0SbgD9BS30owECQjN13lOAyTl/7+MmseeClCduP/Xs/8U+YNaZ6Rqq000iwHPgts6xMS+0O0PIrHhBA3AJXAp8dBt4SmrqaBP//+IZ557EV0eh3XffFKrr3hU6osd4JTd7COR3/9Hw5+cgir3cqnvnEZC1fNP+boIiEEuSU5w4p4Gguc7V288+S7vPbI6wT8QZauXsx5XziXjFz1+3osuLvdbHxjc5R8z8f7hmwc4mHMjYOU8j2gPwfDGWOpy0TjtRff5qlH1wAQDAa5/55/Mn3WVM658PRx1kzRHx6Xh6f+9CxJKUmc/dnVeFwenv7TM2QXZVE6c3IXSNy/9QAv/f2V8PG6VzaQW5LD6mvOHEet+sfZ5qSrvQt7qn1cy5/3h9FspGxWCfWH6iPkBVNGp0WxSlWcIHi9XtY8/VqU/N03o2IGFAlEZ2sn0xZMpbujm5cffpV1r2xg5WWn0tF8zNHgE4bdG6NjVza8/jEel2cctBmY/VsP8Nuv/56fXf9Lfvv1P7B/W+L1MtMb9Jx62Sk40o70Ni+aVkjFommjcj21YR0HgUCA2up6pIT8wpxx2ec3Go3MWzSbHdt2R8hnzqkYc10U8WO0mNi5fhcHPzkEQE9nD8/f/wJf+cVNA594DLi73bS3dGC1WUjJHF4Gdn/0OF10tnZis1tJHiRCqqA8+o22ZGZJwiXrtdS1cO9/3U9PZw8ATTVN3PdfD/Ddv3wr4bbACqcW8O17bqXuUD0Go4G8srxRi1RTxmEQWppa+ddDT/LgXx5FSsm1X/gUn73xCrKyM8ZUDyEEn7r6Al594S1amrQEqvKpJaxYtWyQMxXjic/tZe+mfVHyzraRbUJfe6COx373OPu3HiA5I5mrvv1pZp0wc0TDHKv3VvPIrx+jclcVqVmpfOZ7VzJ9cUW/tZKmL64gvzwvnKBnT7Vz6iUr0OkTa8Oipb41bBh66e7oprW+NeGMA0BGXgYZeaP//FHGYQA6Ojp598213PeHh8OyB//yKGVTirn0yvOPed5AIEBdTQNCCPIKcuIqRNbW2o7ZYuaBf/+OykM16PV6pk4vIzdP5QomMhabhYy8DFqOKrQ3km97rm4Xj/76sfDqpLOlk7/+99/47l++NWjIbGtDKwF/gLTsNAzG/h8HXR3d/P2n/6QutN/d3tTOvT/8K9+/7zvklsZuRZtVmMVXfnkTtQfqCPgD5JXmkpmfeWw3OYrYU5LQ6XUEA8GwTKfXkZR8fPfATiwTnkBs27yTn/+/3/Hy829Effb8U68e87xNjS3c/av7ufiMz3Hpmddx3x8eprVl4PqCG9du4brLv86Fq67htlv+j8ysdFasWqYMwwTAkebgym9+KuINftGqBRROHbmObu1NHWHD0EswEKSpuqnfc1zdLt596j1+/oU7+b/P3cF/fvcErfX9l/Roa2wLG4Ze/L4ATYNkZqdmpjLrhJnMPXlOQhoGgKyibC6+6YII2cVfupDsouM7j0qtHGLQUNfILV/8IQLBaWetoLA4nzPPPRWB4I1X3mXO/BmDT9IP777xEfff808AfMAff/MAxWWFnHtR7ECtykM13HzDD+hydgOwc9sevvXl/8c/nv7TmG9tKY6N6Usq+O5fvkVjdRM2h42CKXnYU+yDnxgnQgjsqXa6jkqGSkrp/8330I7DPHbXE+HjD9Z8REpWCud9/pyY461JViw2M+6eSGdyUvLE7wltNBpYfvHJlM0po72pnbTsNPLKcwdcSR0PqJVDDGqq6mlubKWpsYW5C2exavXJ/PvvT/PIQ09y0qlLOaefB/lgBINBnn3ipSj5qy+8PYAutWHD0EtdTQN11fX9nKFINHQ6HQVT8lm4cj7TF00bMcPgcXn46MV13Ptf93PhF89H6I5EiC85czH5Zf2HOB6M0WNi3csb6O7ojjEaMvMz+PStl0fITr10BXllsbeUJhpmi5my2aUsXLWA0lklmC2qU93xbRr7ITnFjsGgR6fXU1tdzz/ufzz82aMPPcXseTOYOXvo4WM6nY5Zcyv4eN3WCPn0WVMG0CU63tpoMmJPHrk3T8Xo0dnaic/jIyUzZcTfRCt3VfLPXzzCnJNns3P9Ls67/hz8Xj8Go4HqfTV0tHRgT439e5KeG73qzCvNxWQ1xRitsXDVAnKLc2iubcGR7qBgSj7WpGPP9FYkNmrlEIOS8iK+8b2bmDa9nG2bdkR9/tzj0W//8XLxp84lLf1ImGFObhZnnHNqv+PLp5Zyw1eviZB9+4dfoaRs5PasFSOPz+tj01ub+cUXf83/XPszHv3Nf2g+xkqs/XF4VxUABVPy2fzWFtbc/yIvP/wqax54kS3vbKWxuv/rTZlXTn6fUFOT1cRZnz1zwDBTo8lI8YxiFp2+kGkLpmJzTPwtJUX/qJVDDIxGI1dcexFLTpzPy2ve5L231kZ8Pv0YVg3hc2dN5eGn7mHPzv3odDoqZk6hsLj/omlWm4Xrv/IZlq88gYb6ZgqLcqmYORW9fnQqMSqicbZ34XV7SclIjvvtv2pPNQ/85KHw8doX12Eym7j865eMWHhpSqibnLvHgy3ZFhWOOdDDO6sgky/f8UVq9tfidfvIL8vtN+pIcXyijEM/2JJszJk/E7PZzPNPvhrOLUhNS+Giy88e1tzFpYUUl8b/5p+cbGfJiQuGdU3F0An4A+xcv4vH73qStqZ2lp29lNXXnElWweBRN0dH9gCsfXkdq685g7Ss1BHRr3x2KQVT8tn4+sec/ulVrHngRXoLaS5atWDQsgpp2WmkZaeNiC6KyYcyDoMwbUY5Dz1+N3t27kdKScXMKWpL5zihem8N9/7w/vAD98MXtBXkFd/6VDhL3u/z09nqxGw1RcTFx4qRz8zLwGzpf09/qGTkZfClO26kuaYFodPx9d98hY7mThxpdgqmFIxoRJTi+EMZhzgoLi2guPTY6u8rJi71lfUcXdJ+7cvrOee6s0nPSaOpppnX/vU6617dQHpOOpfffAnTF1WgN+gpnl5EyYxiDu+qBLSkqku/evGI7tP7PD4OfXKYp+55hu7OHk65ZDkrLz2FtBy1GlAMH2UcFIp+sNptGE1G5p0yl+Q0B7s27sbn8WEyG/H7/Lz8j1dZ++I6ABqrGvnLD/7Kt++5leLpRaTnpHHD/1xP9b5q3N1ucktyKJgysi8Yh3dVRvg1Xn/0TUwWU7+5CgrFUFDGQaHoh8JpBVz2tYt564l3aG/qYO6KOZx03jLsqXZa6lpY/0pko6lgIEj94XqKpxcBkJadSlp26qjpd3jn4SjZB89/xCkXLceRnnglpxUTC2UcFIp+6Gpz8p/fPxmuubPh1Y1YbGamzC3HaDGRnJ5Me1N7xDljGfcfywBk5KZFtCQ9XnC2O5ESkhOwD8NEReU5KBT9UHeoPqIYG8CHa9bS0dJBcpqDT33jMrSutxqls0sprBi7YIWy2WVkFR6p/6PT6zj/hvMw246f7F5Xl4uPXlzLnTf9hl/c+Cveffo9ujtjZ3krhoZaOSgU/WCJsQpIzkjGaDbi6nZRMrOYb/3xFuoP12Nz2CiqKBqxMNV4yCrI5Gt3fonKPdV43R7yy/IoGKQK62Rj35b9/PMXj4aPH/vdE9gcNhafsWgctZocKOOgUPRD0bRCiqYVUrW3GtAK3F317U9Tu7+OZ+59nraGNk4+/0SWX3QS6TnjU/d/rGr7Jyob3/g4Svb+cx+ycNWChOsbMdE47o1DR4cTGZSkpiVHyqQkNXV0OiwpJgbpOWnc+H/XU7m7ih6ni7zSXHR6Hb/52l3h7aZX/vkaPp+fS750oXoYjQMZMWpEZRZkqv+LEeC4/Ql2d/Xw4rOvc+3FX+bqC2/iqX+vobGxhReeeY1rL/4yn7nwSzz12At0doxsxy7FxCI9J50Fp87n5PNPpGx2KfWHG6L8EO898z4dLZO/J3QismDVfKx2S/jYZDGx/IITx1GjycNxu3L4eN1Wvn/z/4SPb7/tV/zstz/itlv+Nyz78Xd/gd2exOrzVg55fp/Xh7OzC0eKHaMxsXrmKo6dWJFA9lQ7BpMBj8uDz+PrtxLqQLh7PPi9x3bu8UzRtEK++YdvULmnChmUFFUUjng+yfHKcWscXnzu9YjjqdPLePWFt6LGPf6vZznjnFPiauXZy97dB3joL4/y0XsbOXnlCXzui1cwtaJsuCorEoCiaYXkFGfTUNkYln3q5kupP9TAmgdepLWhjRUXnsTSs5bGleMQDATZu3kfL/ztJdqb2znl4hUsWb2Y1MyUQc9VaOSV5ZE3QO8KxbFx3BqHnNzIFoBdzm4ysqLLDuTkZQ/JMDQ1NPPNm/6bykOaE/Ppx15g68c7uP/R35KRNbDT0unsRicgyR5dl8fv89Pl7MaRYlcVWccRV5eLOSfPZtFpC/G6vdiSbQQDQf74nT8T8AcAeO6vL+B1+zjvC+cM+rtTtbeae773l/BW1TN/eY6AP8DZn1096veiUAzEcetzOOu8Vdj6hCo2N7Wy+vzTImRms4lPfebCIc1beagmbBh6ObDvEJWHavo9x9nZxZqnXuVzl36V66+4hddefAdXjzv8+Z6d+/nx937BVRfexC9+8gcO7ovOjFWMDbUH63j90Td56eFXeOep93juvjXUHqgLG4Ze3n7yHTpaOgedr2pPdZQP460n4jtXoRhNjtuVw8y5Ffz9iT+y5eNPCPgDzFs8m5mzp/HQ43ez5eNPCAaCLFk2n6zcTPx+f7gK52D0115woLaDaz/4mB/c+n/h4299+b/588O/4uRTl9JQ18jNN/yAupoGAB79+1N8sm039zz0C1JSVDTVWGOyaP4jGZT4vD6AmP0ZbMk2DHH0bTBbo38vkpKTMBjV6lAxvhy3xgGgYuYUKmZGtuicPmsq02dN5eC+w/z7H0/z7htrOWnFYq7+/GVMicNvUDa1mIs/fS7P/OfFsOzyqy+gbEpRzPGBQIDHHn46Sv7C069x8qlLOXSgKmwYetm2aQeVB2uYu0AZh7GmaFohmfmZEV3dimcUkZGXTktda1h2yZcvwhFHKYeSmcWkZaXS1qcMx0VfOj9myW+FYiw5ro1Df7S1tnPbLf/Hzu17AKg6XMP6jzZz/6O/IzN74ISjpCQbt3zvi5y2ejl7dh2gYuYU5i+chdUWu+aOTqcjPTPa19Hr/zCbo98shRAj2hdAET+Z+Zl85Zc3cWD7QTpbOimbXUrJjGK+eueXOLDtIJ2tTspml1I6sySu+bILs/jab77CgW0H6WrvonxOGcUzYr9IKBRjiTIOMag8VBM2DL0c3F/JoQNVgxoHgMzsDE4/+xROP/uUQccKIbjqs5fyypq38Pv8AFgsZs46fxUA5VNLOP3sFbzx8nvhc67+/KWq4dA4kl2YRXZh1lGybLILs49pvpyibHKKju1chWK0UMYhBqZ+mqybzKPztj5v0Sz+/sTdrP9oM3q9nqUnLmDmnAoAklMd3Hb7LZxz4Rns23OQWXMqmL94TswVhSKx6ersxmwxYezn90uhSCSUcYhBaXkRl1xxHk8/9kJYtvq8VZRNKR6V6+l0OubMn8mc+TNjfp6bl805F54+KtdWjD71h+rZ/O5WNr+1hYz8DFZeegoVi6aNt1oKxYAo4xADq83Kzd+5gRWrTuCTrbuZOaeChUvn4khW2auKoeH3+Xnnqfd495n3AajZX8uejXv42q++Qums+PwSCsV4kHDGQQhxDnAXoAf+KqW8Yzz0yMrJ5KzzT+Os808bj8srJgn1lY18+MJHETJ3j4faA7XKOCgSmoRKghNC6IE/AucCs4CrhRCzxlcrheLY0et0GGL4GGLlRigUiURCGQfgBGCflPKAlNILPApcPM46KRTHTF5ZLquvjvQXpeWkUThNFYdTJDaJtq1UAFT1Oa4GlvUdIIS4CbgJoLh4dBzECsVIcsLZS0nPTWfXhj1kFWQwY8l0VTlUkfAkmnEQMWQy4kDKe4F7AZYsWSJjjFcoEorUrFSWnLmYJWcuHm9VFIq4SbRtpWqgb3poIVA7TrooFArFcUuiGYf1wDQhRJkQwgRcBTw7zjopFArFcUdCbStJKf1CiK8DL6OFsj4gpfxknNVSKBSK446EMg4AUsoXgBcGHahQKBSKUSPRtpUUCoVCkQAo46BQKBSKKISUEzcaVAjRBIxlz8xMoHnQUYmPuo/EQt1H4jAZ7gEGv48SKWXWAJ9PbOMw1gghNkgpl4y3HsNF3Udioe4jcZgM9wAjcx9qW0mhUCgUUSjjoFAoFIoolHEYGveOtwIjhLqPxELdR+IwGe4BRuA+lM9BoVAoFFGolYNCoVAoolDGQaFQKBRRKOMQAyFEkRDiTSHETiHEJ0KIW0LydCHEq0KIvaGvaeOtazwIIfRCiE1CiOdDxxPuPoQQqUKIx4UQu0L/LydN0Pv4Zuh3arsQ4hEhhGUi3IcQ4gEhRKMQYnsfWb96CyF+IITYJ4TYLYQ4e3y0jqaf+7gz9Hu1VQjxlBAitc9nE+Y++nz2HSGEFEJk9pEN+T6UcYiNH/i2lHImcCLwtVC70tuA16WU04DXQ8cTgVuAnX2OJ+J93AW8JKWcAcxHu58JdR9CiALgG8ASKeUctOKSVzEx7uNB4JyjZDH1Dv2tXAXMDp1zT6gFcCLwINH38SowR0o5D9gD/AAm5H0ghCgCVgOVfWTHdB/KOMRASlknpfw49L0T7UFUgNay9KHQsIeAS8ZFwSEghCgEzgf+2kc8oe5DCJEMnArcDyCl9Eop25lg9xHCAFiFEAbAhtavJOHvQ0r5DtB6lLg/vS8GHpVSeqSUB4F9aC2Ax51Y9yGlfEVK6Q8dfoTWRwYm2H2E+C3wPSKbpB3TfSjjMAhCiFJgIbAWyJFS1oFmQIDscVQtXn6H9ssS7CObaPdRDjQBfwttj/1VCJHEBLsPKWUN8Cu0t7o6oENK+QoT7D760J/esdr9TpS+qF8AXgx9P6HuQwhxEVAjpdxy1EfHdB/KOAyAEMIOPAHcKqXsHG99hooQ4gKgUUq5cbx1GSYGYBHwJynlQqCbxNx6GZDQnvzFQBmQDyQJIa4dX61GhUHb/SYiQogfoW0p/7NXFGNYQt6HEMIG/Aj4f7E+jiEb9D6UcegHIYQRzTD8U0r5ZEjcIITIC32eBzSOl35xshy4SAhxCHgUOF0I8Q8m3n1UA9VSyrWh48fRjMVEu48zgYNSyiYppQ94EjiZiXcfvfSn94Rr9yuEuA64ALhGHkn+mkj3MQXtpWNL6O+9EPhYCJHLMd6HMg4xEEIItP3tnVLK3/T56FngutD31wHPjLVuQ0FK+QMpZaGUshTNIfWGlPJaJt591ANVQojpIdEZwA4m2H2gbSedKISwhX7HzkDzZ020++ilP72fBa4SQpiFEGXANGDdOOgXF0KIc4DvAxdJKXv6fDRh7kNKuU1KmS2lLA39vVcDi0J/O8d2H1JK9e+of8AKtGXXVmBz6N95QAZaVMbe0Nf08dZ1CPe0Cng+9P2Euw9gAbAh9H/yNJA2Qe/jdmAXsB14GDBPhPsAHkHzk/hCD54bBtIbbYtjP7AbOHe89R/kPvah7cn3/q3/eSLex1GfHwIyh3MfqnyGQqFQKKJQ20oKhUKhiEIZB4VCoVBEoYyDQqFQKKJQxkGhUCgUUSjjoFAoFIoolHFQJDRCiEtDFSZnjMO1D/WtbDmYPFEQQiwUQvw19P1PhBDfGWDsT446zhJCvDTKKiomAMo4KBKdq4H30JL4FPHxQ+APAw0QQswSQrwDfEUI8bEQ4moAKWUTUCeEWD4GeioSGGUcFAlLqLbVcrREpav6yFcJId4SR/o7/DOUcdz7Vn976IG3rXfFcfQbtND6KZSGvn9aCLFRaH0WbhqCfqVC6y1xX+jcV4QQ1tBnU4UQrwkhtoR0mSI07gxde5sQ4so+9/O2EOIxIcQeIcQdQohrhBDrQuOmhMZlCSGeEEKsD/2LeoALIRzAPBldfA0hxBeFEC+GdPwJ8HfgT6Gf8fo+Q58Gron356CYnCjjoEhkLkHr4bAHaBVCLOrz2ULgVmAWWtXWvg/KZinlIrQHX79bKn34gpRyMbAE+IYQImMIOk4D/iilnA20A5eH5P8Myeej1U+qAy5Dy/Sej1Zn6c7e2kQh2S3AXOCzQIWU8gS0Uus3h8bcBfxWSrk0dJ2+Zdh7WYKWfR2BEOLrwIXAJVJKF+BFq6Kqk1K6pJT7+gzfAJwyhJ+BYhKijIMikbkarWAgoa9X9/lsnZSyWkoZRCt5UNrns95CiRuPkvfHN4QQW9Bq+RehPfDj5aCUcnPf64Xe3guklE8BSCndUqvZswJ4REoZkFI2AG8DS0PnrpdaHxEPWpmDV0LybX3u4UzgbiHEZrR6Ocmha/UlD628eV8+C5wLXB6aH7RaQnOBrwshnhNCzO8zvhGtaqziOMYw3gooFLEIvb2fDswRQki0rmlSCPG90BBPn+EBIn+XPTHkfiJfhiyh66xCe+ieJKXsEUK81ftZnByth5XYJZIZQH70PME+x0GO3IMupKdrgHlcROu/HW3FUggchHBviauFEP+DtqX0JFplT0LnD3QNxXGAWjkoEpVPAX+XUpZIrdJkEdqDbcUxzncIrcw3oe2pspA8BWgLGYYZaG1hh4XUen9UCyEuCV3PLLR6++8AVwqtp3cWWne7oVT5fAX4eu+BEGJBjDE7galHyTYBXwKeFULkh86dHfosiLbiSeozvoIYW1OK4wtlHBSJytXAU0fJngA+c4zzPQGkh7ZkvoLWKxjgJcAghNgK/C/a1tJI8Fm07aqtwAdALtr9bAW2AG8A35NaSeV4+QawRAixVQixA/jy0QOklLuAlKO3m6SU76H5X9aEwnAvE0J8hNb57JXQ3L2cBqwZgl6KSYiqyqpQTDKEEN8EnFLKWA7ro8f+REr5k6Nk7wAXSynbRklFxQRArRwUisnHn4j0YQzEW30PQttdv1GGQaFWDgqFQqGIQq0cFAqFQhGFMg4KhUKhiEIZB4VCoVBEoYyDQqFQKKJQxkGhUCgUUfx/zHBKzdjudX4AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "classify = cluster.KMeans(n_clusters= 5)\n", + "clusters = classify.fit_predict(MallData[['Annual Income (k$)','Spending Score (1-100)']])\n", + "MallData['Cluster'] = clusters\n", + "sns.scatterplot('Annual Income (k$)', 'Spending Score (1-100)', data=MallData, hue='Cluster')\n", + "plt.show()" + ] }, { "cell_type": "markdown", @@ -345,11 +625,20 @@ "## Feedback\n", "If there are any part which is not clear to you, you can write down your questions here." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ı have confusion about the precision of the elbow method" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -363,7 +652,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.7" } }, "nbformat": 4,